Datasets:

librarian-bots

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card_with_first_commit

like 0

Bryson575x/riceboi

--- tags: - generated_from_trainer model-index: - name: first results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # first This model is a fine-tuned version of [nystromformer-gottbert-base-8192](https://huggingface.co/nystromformer-gottbert-base-8192) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5135 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.7133 | 0.1 | 500 | 6.6155 | | 2.7876 | 0.2 | 1000 | 2.5542 | | 2.1831 | 0.3 | 1500 | 2.0356 | | 2.0316 | 0.4 | 2000 | 1.8793 | | 2.0678 | 0.49 | 2500 | 1.7954 | | 1.8182 | 0.59 | 3000 | 1.7473 | | 1.7393 | 0.69 | 3500 | 1.7081 | | 1.7586 | 0.79 | 4000 | 1.6787 | | 1.7417 | 0.89 | 4500 | 1.6563 | | 1.8256 | 0.99 | 5000 | 1.6370 | | 1.7957 | 1.09 | 5500 | 1.6219 | | 1.6876 | 1.19 | 6000 | 1.6084 | | 1.7172 | 1.28 | 6500 | 1.5941 | | 1.6564 | 1.38 | 7000 | 1.5881 | | 1.732 | 1.48 | 7500 | 1.5757 | | 1.8272 | 1.58 | 8000 | 1.5692 | | 1.7951 | 1.68 | 8500 | 1.5617 | | 1.6669 | 1.78 | 9000 | 1.5546 | | 1.6489 | 1.88 | 9500 | 1.5458 | | 1.772 | 1.98 | 10000 | 1.5439 | | 1.7424 | 2.08 | 10500 | 1.5379 | | 1.7077 | 2.17 | 11000 | 1.5322 | | 1.6926 | 2.27 | 11500 | 1.5294 | | 1.656 | 2.37 | 12000 | 1.5274 | | 1.7002 | 2.47 | 12500 | 1.5201 | | 1.7102 | 2.57 | 13000 | 1.5197 | | 1.7158 | 2.67 | 13500 | 1.5162 | | 1.6081 | 2.77 | 14000 | 1.5169 | | 1.754 | 2.87 | 14500 | 1.5140 | | 1.3588 | 2.96 | 15000 | 1.5135 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.1+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0

Bubb-les/DisloGPT-medium-HarryPotter

conversational

--- tags: - conversational --- #Kobayashi DialoGPT Model

BumBelDumBel/ZORK-AI-TEST

text-generation

--- language: es tags: - summarization - spanish - beto2beto - encoder-decoder license: apache-2.0 datasets: - LeoCordoba/CC-NEWS-ES-titles model-index: - name: beto2beto-ccnews-titles-es results: - task: name: Abstractive Text Summarization type: abstractive-text-summarization dataset: name: "CCNEWS-ES-titles" type: LeoCordoba/CC-NEWS-ES-titles metrics: - name: Validation ROGUE-1 type: rogue-1 value: 23.7478 - name: Validation ROGUE-2 type: rogue-2 value: 7.3616 - name: Validation ROGUE-L type: rogue-l value: 20.6615 - name: Validation ROGUE-Lsum type: rogue-lsum value: 20.7371 - name: Test ROGUE-1 type: rogue-1 value: 23.7415 - name: Test ROGUE-2 type: rogue-2 value: 7.3548 - name: Test ROGUE-L type: rogue-l value: 20.746 - name: Test ROGUE-Lsum type: rogue-lsum value: 20.8149 widget: - text: | La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche, por delante del helado de pistacho italiano y la tarta alemana de manzana. “Este postre argentino sin hornear fue influenciado por la cocina italiana y se inspiró en el famoso tiramisú italiano. Está elaborado con tres ingredientes básicos argentinos: galletas de chocolate, dulce de leche y queso crema”, explica la página web que exhorta a los turistas de todo el mundo a que prueben la chocotorta. En la votación, superó también a los waffles belgas y el zserbó húngaro. A nivel local le sigue el alfajor, con 4,2 puntos contra los 4,7 de la torta. En el texto que acompaña al listón dorado de “postre número uno“, los expertos enseñan además cómo se hacen las chocotortas, paso por paso. “Las galletas se ablandan en leche y se cubren con una combinación de queso crema y dulce de leche. Las formas de la chocotorta pueden variar, mientras que las galletas se pueden remojar con leche con chocolate, café o incluso licor de café”, detallan. Por último, adjudican su creación a una “campaña de márketing” diseñada para promover las galletitas icónicas que le dan su nombre. La chocotorta, infaltable en los cumpleaños argentinos, fue creada en 1982 por una creativa de las agencias más importantes del país, Marité Mabragaña. --- ## Hyperparameters { "num_train_epochs": 3, "seed": 7, "summary_column": "output_text", "text_column": "text", "encoder_max_length" : 512, "decoder_max_length" :36, "batch_size" : 256 } ## Usage ## Results | key | value | | --- | ----- | | eval loss | 4.539857387542725| | eval_rouge1 |23.7478 | | eval_rouge2 |7.3616 | | eval_rougeL |20.6615 | | eval_rougeLsum |20.7371 | | eval_gen_len| 16.1806| |test loss | 4.515065670013428| | test_rouge1 | 23.7415| | test_rouge2 | 7.3548| | test_rougeL | 20.746| | test_rougeLsum | 20.8149| | test_gen_len| 16.1926|

BumBelDumBel/ZORK_AI_FANTASY

--- language: es tags: - summarization - spanish - encoder-decoder - beto license: apache-2.0 datasets: - mlsum - es model-index: - name: beto2beto-mlsum results: - task: type: summarization # Required. Example: automatic-speech-recognition name: abstractive summarization # Optional. Example: Speech Recognition dataset: type: mlsum # Required. Example: common_voice. Use dataset id from https://hf.co/datasets name: mlsum-es # Required. Example: Common Voice zh-CN args: es # Optional. Example: zh-CN metrics: - name: rouge1 type: rouge1 value: 25.8639 - name: rouge2 type: rouge2 value: 8.911 - name: rougeL type: rougeL value: 21.2426 - name: rougeLsum type: rougeLsum value: 21.5859 widget: - text: | La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche, por delante del helado de pistacho italiano y la tarta alemana de manzana. “Este postre argentino sin hornear fue influenciado por la cocina italiana y se inspiró en el famoso tiramisú italiano. Está elaborado con tres ingredientes básicos argentinos: galletas de chocolate, dulce de leche y queso crema”, explica la página web que exhorta a los turistas de todo el mundo a que prueben la chocotorta. En la votación, superó también a los waffles belgas y el zserbó húngaro. A nivel local le sigue el alfajor, con 4,2 puntos contra los 4,7 de la torta. En el texto que acompaña al listón dorado de “postre número uno", los expertos enseñan además cómo se hacen las chocotortas, paso por paso. “Las galletas se ablandan en leche y se cubren con una combinación de queso crema y dulce de leche. Las formas de la chocotorta pueden variar, mientras que las galletas se pueden remojar con leche con chocolate, café o incluso licor de café”, detallan. Por último, adjudican su creación a una “campaña de márketing” diseñada para promover las galletitas icónicas que le dan su nombre. La chocotorta, infaltable en los cumpleaños argentinos, fue creada en 1982 por una creativa de las agencias más importantes del país, Marité Mabragaña. --- ## beto2beto-mlsum This model was trained on the Spanish section of MLSum: https://paperswithcode.com/sota/abstractive-text-summarization-on-mlsum. ## Hyperparameters { "dataset_config": "es", "dataset_name": "mlsum", "do_eval": true, "do_predict": true, "do_train": true, "fp16": true, "max_target_length": 64, "num_train_epochs": 10, "per_device_eval_batch_size": 4, "per_device_train_batch_size": 4, "predict_with_generate": true, "sagemaker_container_log_level": 20, "sagemaker_program": "run_summarization.py", "seed": 7, "summary_column": "summary", "text_column": "text" } ## Usage ## Results | metric | score | | --- | ----- | | validation_loss | 2.5021677017211914 | | validation_rouge1 | 26.1256 | | validation_rouge2 | 9.2552 | | validation_rougeL | 21.4899 | | validation_rougeLsum | 21.8194 | | test_loss | 2.57672381401062 | | test_rouge1 | 25.8639 | | test_rouge2 | 8.911 | | test_rougeL | 21.2426 | | test_rougeLsum | 21.5859 |

BumBelDumBel/ZORK_AI_SCIFI

text-generation

--- language: es tags: - text-generation - spanish - encoder-decoder - beto license: apache-2.0 datasets: - LeoCordoba/CC-NEWS-ES model-index: - name: beto2beto --- ## beto2beto Usage example here: https://colab.research.google.com/drive/18a2ZfF1e_Kyyydlv8INQIkJbv294xcAm?usp=sharing Entrenado por 3 epochs sobre CC-NEWS-ES (2019), aproximadamente 68.000 steps. Encoder max length: 40•Decoder max length: 128 ## Hyperparameters ## Usage ## Results | key | value | | --- | ----- | | test_loss | 2.65148806571960452 |

BunakovD/sd

--- language: es tags: - summarization - mt5 - spanish license: apache-2.0 datasets: - LeoCordoba/CC-NEWS-ES-titles model-index: - name: mt5-small-ccnews-titles-es results: - task: name: Abstractive Text Summarization type: abstractive-text-summarization dataset: name: "CCNEWS-ES-titles" type: LeoCordoba/CC-NEWS-ES-titles metrics: - name: Validation ROGUE-1 type: rogue-1 value: 22.6623 - name: Validation ROGUE-2 type: rogue-2 value: 7.7894 - name: Validation ROGUE-L type: rogue-l value: 19.8015 - name: Validation ROGUE-Lsum type: rogue-lsum value: 19.8092 - name: Test ROGUE-1 type: rogue-1 value: 22.9263 - name: Test ROGUE-2 type: rogue-2 value: 7.9146 - name: Test ROGUE-L type: rogue-l value: 20.0272 - name: Test ROGUE-Lsum type: rogue-lsum value: 20.0387 widget: - text: "La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche, por delante del helado de pistacho italiano y la tarta alemana de manzana. “Este postre argentino sin hornear fue influenciado por la cocina italiana y se inspiró en el famoso tiramisú italiano. Está elaborado con tres ingredientes básicos argentinos: galletas de chocolate, dulce de leche y queso crema”, explica la página web que exhorta a los turistas de todo el mundo a que prueben la chocotorta. En la votación, superó también a los waffles belgas y el zserbó húngaro. A nivel local le sigue el alfajor, con 4,2 puntos contra los 4,7 de la torta. En el texto que acompaña al listón dorado de “postre número uno“, los expertos enseñan además cómo se hacen las chocotortas, paso por paso. “Las galletas se ablandan en leche y se cubren con una combinación de queso crema y dulce de leche. Las formas de la chocotorta pueden variar, mientras que las galletas se pueden remojar con leche con chocolate, café o incluso licor de café”, detallan. Por último, adjudican su creación a una “campaña de márketing” diseñada para promover las galletitas icónicas que le dan su nombre. La chocotorta, infaltable en los cumpleaños argentinos, fue creada en 1982 por una creativa de las agencias más importantes del país, Marité Mabragaña." --- ## Hyperparameters { "max_target_length": 64, "model_name_or_path": "google/mt5-small", "num_train_epochs": 3, "seed": 7, "summary_column": "output_text", "text_column": "text", "encoder_max_length" : 512, "decoder_max_length" :36, "batch_size" : 128 } ## Usage ``` article = """ La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche, por delante del helado de pistacho italiano y la tarta alemana de manzana. “Este postre argentino sin hornear fue influenciado por la cocina italiana y se inspiró en el famoso tiramisú italiano. Está elaborado con tres ingredientes básicos argentinos: galletas de chocolate, dulce de leche y queso crema”, explica la página web que exhorta a los turistas de todo el mundo a que prueben la chocotorta. En la votación, superó también a los waffles belgas y el zserbó húngaro. A nivel local le sigue el alfajor, con 4,2 puntos contra los 4,7 de la torta. En el texto que acompaña al listón dorado de “postre número uno", los expertos enseñan además cómo se hacen las chocotortas, paso por paso. “Las galletas se ablandan en leche y se cubren con una combinación de queso crema y dulce de leche. Las formas de la chocotorta pueden variar, mientras que las galletas se pueden remojar con leche con chocolate, café o incluso licor de café”, detallan. Por último, adjudican su creación a una “campaña de márketing” diseñada para promover las galletitas icónicas que le dan su nombre. La chocotorta, infaltable en los cumpleaños argentinos, fue creada en 1982 por una creativa de las agencias más importantes del país, Marité Mabragaña. """ from transformers import pipeline summarizer = pipeline("summarization", model="LeoCordoba/mt5-small-ccnews-titles-es") summarizer(article, min_length=5, max_length=64) ``` ## Results | metric | score | | --- | ----- | | eval_loss | 2.879085063934326 | | eval_rouge1 | 22.6623 | | eval_rouge2 | 7.7894 | | eval_rougeL | 19.8015, | | eval_rougeLsum | 19.8092 | | eval_gen_len | 17.1839 | | test_loss | 2.878429412841797 | | test_rouge1 | 22.9263 | | test_rouge2 | 7.9146 | | test_rougeL | 20.0272 | | test_rougeLsum | 20.0387 | | test_gen_len | 17.1696 |

Buntan/BuntanAI

--- language: es tags: - summarization - sagemaker - mt5 - spanish license: apache-2.0 datasets: - mlsum - es model-index: - name: mt5-small-mlsum results: - task: type: summarization # Required. Example: automatic-speech-recognition name: abstractive summarization # Optional. Example: Speech Recognition dataset: type: mlsum # Required. Example: common_voice. Use dataset id from https://hf.co/datasets name: mlsum-es # Required. Example: Common Voice zh-CN args: es # Optional. Example: zh-CN metrics: - name: rouge1 type: rouge1 value: 26.0756 - name: rouge2 type: rouge2 value: 8.4669 - name: rougeL type: rougeL value: 20.8167 - name: rougeLsum type: rougeLsum value: 21.0822 widget: - text: "La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche, por delante del helado de pistacho italiano y la tarta alemana de manzana. “Este postre argentino sin hornear fue influenciado por la cocina italiana y se inspiró en el famoso tiramisú italiano. Está elaborado con tres ingredientes básicos argentinos: galletas de chocolate, dulce de leche y queso crema”, explica la página web que exhorta a los turistas de todo el mundo a que prueben la chocotorta. En la votación, superó también a los waffles belgas y el zserbó húngaro. A nivel local le sigue el alfajor, con 4,2 puntos contra los 4,7 de la torta. En el texto que acompaña al listón dorado de “postre número uno“, los expertos enseñan además cómo se hacen las chocotortas, paso por paso. “Las galletas se ablandan en leche y se cubren con una combinación de queso crema y dulce de leche. Las formas de la chocotorta pueden variar, mientras que las galletas se pueden remojar con leche con chocolate, café o incluso licor de café”, detallan. Por último, adjudican su creación a una “campaña de márketing” diseñada para promover las galletitas icónicas que le dan su nombre. La chocotorta, infaltable en los cumpleaños argentinos, fue creada en 1982 por una creativa de las agencias más importantes del país, Marité Mabragaña." --- ## mt5-small-mlsum This model was trained on the Spanish section of MLSum: https://paperswithcode.com/sota/abstractive-text-summarization-on-mlsum based on mt5-small. ## Hyperparameters { "dataset_config": "es", "dataset_name": "mlsum", "do_eval": true, "do_predict": true, "do_train": true, "fp16": true, "max_target_length": 64, "model_name_or_path": "google/mt5-small", "num_train_epochs": 10, "output_dir": "/opt/ml/checkpoints", "per_device_eval_batch_size": 4, "per_device_train_batch_size": 4, "predict_with_generate": true, "sagemaker_container_log_level": 20, "sagemaker_program": "run_summarization.py", "save_strategy": "epoch", "seed": 7, "summary_column": "summary", "text_column": "text" } ## Usage ``` article = """ La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche, por delante del helado de pistacho italiano y la tarta alemana de manzana. “Este postre argentino sin hornear fue influenciado por la cocina italiana y se inspiró en el famoso tiramisú italiano. Está elaborado con tres ingredientes básicos argentinos: galletas de chocolate, dulce de leche y queso crema”, explica la página web que exhorta a los turistas de todo el mundo a que prueben la chocotorta. En la votación, superó también a los waffles belgas y el zserbó húngaro. A nivel local le sigue el alfajor, con 4,2 puntos contra los 4,7 de la torta. En el texto que acompaña al listón dorado de “postre número uno", los expertos enseñan además cómo se hacen las chocotortas, paso por paso. “Las galletas se ablandan en leche y se cubren con una combinación de queso crema y dulce de leche. Las formas de la chocotorta pueden variar, mientras que las galletas se pueden remojar con leche con chocolate, café o incluso licor de café”, detallan. Por último, adjudican su creación a una “campaña de márketing” diseñada para promover las galletitas icónicas que le dan su nombre. La chocotorta, infaltable en los cumpleaños argentinos, fue creada en 1982 por una creativa de las agencias más importantes del país, Marité Mabragaña. """ from transformers import pipeline summarizer = pipeline("summarization", model="LeoCordoba/mt5-small-mlsum") summarizer(article, min_length=5, max_length=64) ``` result: [{'summary_text': 'El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche'}] ## Results | metric | score | | --- | ----- | | eval_rouge1 | 26.4352 | | eval_rouge2 | 8.9293 | | eval_rougeL | 21.2622 | | eval_rougeLsum | 21.5518 | | test_rouge1 | 26.0756 | | test_rouge2 | 8.4669 | | test_rougeL | 20.8167 | | test_rougeLsum | 21.0822 |

Buntan/xlm-roberta-base-finetuned-marc-en

--- tags: - conversational --- This is Chandler. Chandler is your friend too.

CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-glf

token-classification

--- tags: - conversational --- # Michael DialoGPT Model

CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa

token-classification

--- thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png tags: - conversational license: mit --- ## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT) DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test. The model is trained on 147M multi-turn dialogue from Reddit discussion thread. * Multi-turn generation examples from an interactive environment: |Role | Response | |---------|--------| |User | Does money buy happiness? | | Bot | Depends how much money you spend on it .| |User | What is the best way to buy happiness ? | | Bot | You just have to be a millionaire by your early 20s, then you can be happy . | |User |This is so difficult ! | | Bot | You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money | Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT) ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536) ### How to use Now we are ready to try out how the model works as a chatting partner! ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large") model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large") # Let's chat for 5 lines for step in range(5): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) # pretty print last ouput tokens from bot print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```

CAMeL-Lab/bert-base-arabic-camelbert-da-pos-egy

token-classification

--- tags: - conversational --- # Jake99 DialoGPT model

CAMeL-Lab/bert-base-arabic-camelbert-da-pos-msa

token-classification

[bert-base-uncased](https://huggingface.co/bert-base-uncased) fine-tuned on the [QNLI](https://huggingface.co/datasets/glue) dataset for 2 epochs. The fine-tuning process was performed on 2x NVIDIA GeForce GTX 1080 Ti GPUs (11GB). The parameters are: ``` max_seq_length=512 per_device_train_batch_size=8 gradient_accumulation_steps=2 total train batch size (w. parallel, distributed & accumulation) = 32 learning_rate=3e-5 ``` ## Evaluation results eval_accuracy = 0.916895 ## More information The QNLI (Question-answering NLI) dataset is a Natural Language Inference dataset automatically derived from the Stanford Question Answering Dataset v1.1 (SQuAD). SQuAD v1.1 consists of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The dataset was converted into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue. The QNLI dataset is part of GLEU benchmark. (source: https://paperswithcode.com/dataset/qnli)

CBreit00/DialoGPT_small_Rick

--- tags: - conversational --- # Peter from Your Boyfriend Game.

CLAck/en-vi

translation

--- language: - qu tags: - Llamacha --- # QuBERTa QuBERTa es un modelo de lenguaje basado en RoBERTa para el quechua. Nuestro modelo de lenguaje fue pre-entrenado con 5M de tokens del quechua sureño (Collao y Chanka). El modelo utiliza un tokenizador Byte-level BPE con un vocabulario de 52000 tokens de subpalabras. ## Usabilidad Una vez descargado los pesos y el tokenizador es necesario adjuntarlo en un sola carpeta, en este caso fue `QuBERTa `. ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="./QuBERTa", tokenizer="./QuBERTa" ) ``` Se hace la prueba, la cual esta en fases de mejoras. ```python fill_mask("allinllachu <mask> allinlla huk wasipita.") ``` [{'score': 0.23992203176021576, 'sequence': 'allinllachu nisqaqa allinlla huk wasipita.', 'token': 334, 'token_str': ' nisqaqa'}, {'score': 0.061005301773548126, 'sequence': 'allinllachu, allinlla huk wasipita.', 'token': 16, 'token_str': ','}, {'score': 0.028720015659928322, 'sequence': "allinllachu' allinlla huk wasipita.", 'token': 11, 'token_str': "'"}, {'score': 0.012927944771945477, 'sequence': 'allinllachu kay allinlla huk wasipita.', 'token': 377, 'token_str': ' kay'}, {'score': 0.01230092253535986, 'sequence': 'allinllachu. allinlla huk wasipita.', 'token': 18, 'token_str': '.'}]

CLTL/icf-levels-adm

text-classification

--- language: it --- # GePpeTto GPT2 Model 🇮🇹 Pretrained GPT2 117M model for Italian. You can find further details in the paper: Lorenzo De Mattei, Michele Cafagna, Felice Dell’Orletta, Malvina Nissim, Marco Guerini "GePpeTto Carves Italian into a Language Model", arXiv preprint. Pdf available at: https://arxiv.org/abs/2004.14253 ## Pretraining Corpus The pretraining set comprises two main sources. The first one is a dump of Italian Wikipedia (November 2019), consisting of 2.8GB of text. The second one is the ItWac corpus (Baroni et al., 2009), which amounts to 11GB of web texts. This collection provides a mix of standard and less standard Italian, on a rather wide chronological span, with older texts than the Wikipedia dump (the latter stretches only to the late 2000s). ## Pretraining details This model was trained using GPT2's Hugging Face implemenation on 4 NVIDIA Tesla T4 GPU for 620k steps. Training parameters: - GPT-2 small configuration - vocabulary size: 30k - Batch size: 32 - Block size: 100 - Adam Optimizer - Initial learning rate: 5e-5 - Warm up steps: 10k ## Perplexity scores | Domain | Perplexity | |---|---| | Wikipedia | 26.1052 | | ItWac | 30.3965 | | Legal | 37.2197 | | News | 45.3859 | | Social Media | 84.6408 | For further details, qualitative analysis and human evaluation check out: https://arxiv.org/abs/2004.14253 ## Load Pretrained Model You can use this model by installing Huggingface library `transformers`. And you can use it directly by initializing it like this: ```python from transformers import GPT2Tokenizer, GPT2Model model = GPT2Model.from_pretrained('LorenzoDeMattei/GePpeTto') tokenizer = GPT2Tokenizer.from_pretrained( 'LorenzoDeMattei/GePpeTto', ) ``` ## Example using GPT2LMHeadModel ```python from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline, GPT2Tokenizer tokenizer = AutoTokenizer.from_pretrained("LorenzoDeMattei/GePpeTto") model = AutoModelWithLMHead.from_pretrained("LorenzoDeMattei/GePpeTto") text_generator = pipeline('text-generation', model=model, tokenizer=tokenizer) prompts = [ "Wikipedia Geppetto", "Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso"] samples_outputs = text_generator( prompts, do_sample=True, max_length=50, top_k=50, top_p=0.95, num_return_sequences=3 ) for i, sample_outputs in enumerate(samples_outputs): print(100 * '-') print("Prompt:", prompts[i]) for sample_output in sample_outputs: print("Sample:", sample_output['generated_text']) print() ``` Output is, ``` ---------------------------------------------------------------------------------------------------- Prompt: Wikipedia Geppetto Sample: Wikipedia Geppetto rosso (film 1920) Geppetto rosso ("The Smokes in the Black") è un film muto del 1920 diretto da Henry H. Leonard. Il film fu prodotto dalla Selig Poly Sample: Wikipedia Geppetto Geppetto ("Geppetto" in piemontese) è un comune italiano di 978 abitanti della provincia di Cuneo in Piemonte. L'abitato, che si trova nel versante valtellinese, si sviluppa nella Sample: Wikipedia Geppetto di Natale (romanzo) Geppetto di Natale è un romanzo di Mario Caiano, pubblicato nel 2012. ---------------------------------------------------------------------------------------------------- Prompt: Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso Sample: Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso. Il burattino riesce a scappare. Dopo aver trovato un prezioso sacchetto si reca Sample: Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso, e l'unico che lo possiede, ma, di fronte a tutte queste prove Sample: Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso: - A voi gli occhi, le guance! A voi il mio pezzo! ``` ## Citation Please use the following bibtex entry: ``` @misc{mattei2020geppetto, title={GePpeTto Carves Italian into a Language Model}, author={Lorenzo De Mattei and Michele Cafagna and Felice Dell'Orletta and Malvina Nissim and Marco Guerini}, year={2020}, eprint={2004.14253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## References Marco Baroni, Silvia Bernardini, Adriano Ferraresi, and Eros Zanchetta. 2009. The WaCky wide web: a collection of very large linguistically processed webcrawled corpora. Language resources and evaluation, 43(3):209–226.

CLTL/icf-levels-mbw

text-classification

--- language: - zh license: - apache-2.0 --- ```python import jieba_fast from transformers import BertTokenizer from transformers import BigBirdModel class JiebaTokenizer(BertTokenizer): def __init__( self, pre_tokenizer=lambda x: jieba_fast.cut(x, HMM=False), *args, **kwargs ): super().__init__(*args, **kwargs) self.pre_tokenizer = pre_tokenizer def _tokenize(self, text, *arg, **kwargs): split_tokens = [] for text in self.pre_tokenizer(text): if text in self.vocab: split_tokens.append(text) else: split_tokens.extend(super()._tokenize(text)) return split_tokens model = BigBirdModel.from_pretrained('Lowin/chinese-bigbird-mini-1024') tokenizer = JiebaTokenizer.from_pretrained('Lowin/chinese-bigbird-mini-1024') ``` https://github.com/LowinLi/chinese-bigbird

CLTL/icf-levels-stm

text-classification

--- language: - zh license: - apache-2.0 --- ```python import jieba_fast from transformers import BertTokenizer from transformers import BigBirdModel class JiebaTokenizer(BertTokenizer): def __init__( self, pre_tokenizer=lambda x: jieba_fast.cut(x, HMM=False), *args, **kwargs ): super().__init__(*args, **kwargs) self.pre_tokenizer = pre_tokenizer def _tokenize(self, text, *arg, **kwargs): split_tokens = [] for text in self.pre_tokenizer(text): if text in self.vocab: split_tokens.append(text) else: split_tokens.extend(super()._tokenize(text)) return split_tokens model = BigBirdModel.from_pretrained('Lowin/chinese-bigbird-small-1024') tokenizer = JiebaTokenizer.from_pretrained('Lowin/chinese-bigbird-small-1024') ``` https://github.com/LowinLi/chinese-bigbird

CM-CA/DialoGPT-small-cartman

--- language: - zh license: - apache-2.0 --- ```python from transformers import BertTokenizer from transformers import BigBirdModel model = BigBirdModel.from_pretrained('Lowin/chinese-bigbird-wwm-base-4096') tokenizer = BertTokenizer.from_pretrained('Lowin/chinese-bigbird-wwm-base-4096') ``` https://github.com/LowinLi/chinese-bigbird

Callidior/bert2bert-base-arxiv-titlegen

summarization

--- tags: - conversational --- # XiaoBot for Discord [Tutorial](https://youtu.be/UjDpW_SOrlw) followed for this model.

CallumRai/HansardGPT2

text-generation

--- language: - pt license: mit tags: - generated_from_trainer model-index: - name: bert-base-portuguese-cased-finetuned-peticoes results: [] widget: - text: "Com efeito, se tal fosse possível, o Poder [MASK] – que não dispõe de função legislativa – passaria a desempenhar atribuição que lhe é institucionalmente estranha (a de legislador positivo), usurpando, desse modo, no contexto de um sistema de poderes essencialmente limitados, competência que não lhe pertence, com evidente transgressão ao princípio constitucional da separação de poderes." --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-portuguese-cased-finetuned-peticoes This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0878 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 215 | 1.1349 | | No log | 2.0 | 430 | 1.0925 | | 1.3219 | 3.0 | 645 | 1.0946 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

CalvinHuang/mt5-small-finetuned-amazon-en-es

summarization

--- language: - pt license: mit tags: - generated_from_trainer model-index: - name: bert-base-portuguese-cased-finetuned-tcu-acordaos results: [] widget: - text: "Com efeito, se tal fosse possível, o Poder [MASK] – que não dispõe de função legislativa – passaria a desempenhar atribuição que lhe é institucionalmente estranha (a de legislador positivo), usurpando, desse modo, no contexto de um sistema de poderes essencialmente limitados, competência que não lhe pertence, com evidente transgressão ao princípio constitucional da separação de poderes." --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-portuguese-cased-finetuned-tcu-acordaos This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5765 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.7308 | 1.0 | 1383 | 0.6286 | | 0.6406 | 2.0 | 2766 | 0.5947 | | 0.6033 | 3.0 | 4149 | 0.5881 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.13.2 - Tokenizers 0.10.3

Cameron/BERT-Jigsaw

text-classification

--- language: - pt license: mit tags: - generated_from_trainer datasets: - lener_br metrics: - precision - recall - f1 - accuracy model_index: - name: bertimbau-base-lener_br results: - task: name: Token Classification type: token-classification dataset: name: lener_br type: lener_br args: lener_br metric: name: Accuracy type: accuracy value: 0.9692504609383333 model-index: - name: Luciano/bertimbau-base-lener_br results: - task: type: token-classification name: Token Classification dataset: name: lener_br type: lener_br config: lener_br split: test metrics: - type: accuracy value: 0.9824282794418222 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDZiZTRmMzRiZDFjOGMzZTM3ODRmNTEwNjI5OTM2ZDhlZjViMDk0YmJjOWViYjM3YmJmZGI2MjJiOTI3OGNmZCIsInZlcnNpb24iOjF9.7DVb3B_moqPXev5yxjcCvBCZDcJdmm3qZsSrp-RVOggLEr_AUfkBrF_76tDVLs9DszD1AlW4ERXcc0ZCqSCaDw - type: precision value: 0.9877557596262284 name: Precision verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTE2MGQ4ZGM1NTEwOGFmMjM3ODAyYTg3MWM1YjVhZGVlYThiNzFjYTE4NWJhOTU3OWZjMjhkODcwNGNiMmIxMyIsInZlcnNpb24iOjF9.G1e_jAOIDcuaOXWNjeRqlHTqJHVc_akZavhyvgBkAPiCTRgoTR24OUu9e_izofDMSTo4xhkMIwsC_O9tKzkNCA - type: recall value: 0.9870401674313772 name: Recall verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTkyZjEwMzk2NTBjY2RhMWVhYWVkOWQ2ZThkZDMwODczMDVkNDI2ZjM3OTA1ODg5NGQyYWUxMGQ5MDRkNjNlNiIsInZlcnNpb24iOjF9.qDL8618-ZTT_iO-eppn7JzVVfd_ayuj4mTT7eIc3zFYKJUp4KNpFgxnjuSVEZTcdOG48YrSISXJoHM5jVXg_DA - type: f1 value: 0.9873978338768773 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjYwOWZkZmFiMTRjY2UyOTJmMDNjMzkzNjUxYTAzYzM2ZDNkMmU0NTQ5NDlmMzU5YWExMDNiZjUzOGVlZjc1OSIsInZlcnNpb24iOjF9.T7MDH4H4E6eiLZot4W_tNzVgi-ctOrSb148x9WttkJFaxh-2P4kNmm4bKJhF1ZZZKgja80hKp_Nm9dmqXU7gAg - type: loss value: 0.11542011797428131 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDA3OGRkY2Q2MjlkZWZlZTVhZDk0MjY3MDA0MzgwZjI4MTk3Y2Q2ZmRkMGI3OTQwMzcyMzVjMGE5MzU4ODY5MiIsInZlcnNpb24iOjF9.nHtVSN-vvFjDRCWC5dXPf8dmk9Rrj-JNqvehDSGCAGLl3WknpwNHzCrJM9sNlRiNgwEIA4ekBHOC_V_OHhp7Bw - task: type: token-classification name: Token Classification dataset: name: lener_br type: lener_br config: lener_br split: validation metrics: - type: accuracy value: 0.9692504609383333 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjY2N2VkZTIyMWM2ZTUxYzFiNjFhNzgwODgzNDQxNTMwODczMThjZDE5MzE3MTllN2ZlNjc4OWI0YTY0NzJkNCIsInZlcnNpb24iOjF9._atPyYtbN7AmDCZHNQHeBDFolzgKbQ04C1c1gfNBomkxlLXiZUVDSPwCNP9fveXhnXwkDsoy3hfm44BTsHtBAw - type: precision value: 0.9786866842043531 name: Precision verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGQzMjM1M2U2MzZiZjJmNGQ1NmUxNjE0NWYyOWJkNGM3NmE0NDg2MjAwZGNkNGZmZDEwMjkwZGQ1MDgyMWU3ZSIsInZlcnNpb24iOjF9.1XNuw2s47lqZD-ywmdEcI6UpPyl_aR-8cxlU1laQYEsUNW1fEZwB90sr7cSbNNTndzEsuH9VzeKgHwlHarq7Dg - type: recall value: 0.9840619998315222 name: Recall verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjllM2VlZTI5NzZlNGFhMjIyN2ZmYmQzNzQ2NDYxZWNkMzY5NzM0YTY3MDE2OTMxMjdiYzkwNjc1ZjBkNDRjYSIsInZlcnNpb24iOjF9.C7SeMwbtrmD24YWsYsxi4RRaVSsuQU-Rj83b-vZ8_H1IggmyNMpv8Y2z1mDh6b5UgaHpuk9YQb9aRKbQuCjTCA - type: f1 value: 0.9813669814173863 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDZjNjNiZjRhNThhNzBiMDNmODIyOTM0YjEwNWVhZTQ5MWRiYzU2ZjBkOGY3NzgzOGE2ZTJkOTNhZWZlMzgxYyIsInZlcnNpb24iOjF9.YDySY0KSF3PieEXXjx1y6GsXr9PQVNF1RW_zAQNTPcbgU8OEwyts_tUXFIT61QVGVchFOG4bLFs0ggOuwvZKBA - type: loss value: 0.22302456200122833 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzFhNTFiYzE1ZjY4MmRjMTI5NGY2YWEyYzY4NzBkYTVjMTk0MWVkODBhY2M0NWQ0ZjM1MmVjZTRmM2RhOTUxZiIsInZlcnNpb24iOjF9.-AXmb23GEbxQ282y9wL-Xvv5cZg0Z3SGQQks5As_BrXlCf8ay8sgd1VWEB4NTepn8MnKJgJkqyQK4JXxSSYCCQ - task: type: token-classification name: Token Classification dataset: name: lener_br type: lener_br config: lener_br split: train metrics: - type: accuracy value: 0.9990127507699392 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODEwMWUyNjU0ZjUyODQ2ZjQ3Y2VjOWY5YWNmZDczMDhhYzZiY2ZjMTFmZTUyZDZhOWJhMjcwMWJlZWNmMDIwOSIsInZlcnNpb24iOjF9.acwBn2no3TJ2cMGaGbQlNn9smS9XTsfKUat5JsKUVHTJa4H6okb5W6Va67KkrT383paAHOkoipb1wJwWfsseCg - type: precision value: 0.9992300721767728 name: Precision verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmQyNDJhNTgzNjc4OWQ5ODcwN2RjM2JhZmNjODljZjIyYWI3MGIyOGNiYWYxNzczNDQyNTZjMDhiODYyYWRiMyIsInZlcnNpb24iOjF9.Z_W8fuCgV5KWChMZXaoJtX-u-SxBd8GcfVXBjFnf7BYqrWoTkcczJqJP1g74Gjrp6xp_VatQ-V1Por5Yzd3dCQ - type: recall value: 0.9993028952029684 name: Recall verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2ZiMjE4NDE0NmI1NjVhNzIyYjJjMTUyZDU2OGY3NTgyYTNhZDBjNWMzYWZmMmI5ZjczZjgyYmZjOGM0YTcyMiIsInZlcnNpb24iOjF9.jB5kEKsJMs40YVJ0RmFENEbKINKreAJN-EYeRrQMCwOrfTXxyxq0-cwgF_T2UJ1vl4eL-MAV2Lc3p449gaDUCg - type: f1 value: 0.9992664823630992 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTQzMWRkZjIyNDY1NzU2NDNmNWJlMDIxOTY4Y2UyYjJlOTVkNTEwZGEwODdjZDMwYTg5ODE3NTlhN2JjMjZlZCIsInZlcnNpb24iOjF9.DspzVgqZh5jbRfx-89Ygh7dbbPBsiLyOostyQ4el1SIoGVRtEfxzYk780hEIRqqagWk63DXY3_eLIRyiBFf8BQ - type: loss value: 0.0035279043950140476 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGQ1OWQxNjNmYzNlMzliODljNTY2YWNhMTUzNjVkMzA0NDYzZWY0ODFiMDlmZWZhNDlkODEyYWU5OWY3YjQyOSIsInZlcnNpb24iOjF9.6S7KwMDEBMWG95o3M0kOnKofgVnPwX8Sf2bQiXns-kZkcrOTXJCq7czloDbSk9d9-sumdxXYk9-oQFDfR6DTAw --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bertimbau-base-lener_br This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the lener_br dataset. It achieves the following results on the evaluation set: - Loss: 0.2298 - Precision: 0.8501 - Recall: 0.9138 - F1: 0.8808 - Accuracy: 0.9693 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0686 | 1.0 | 1957 | 0.1399 | 0.7759 | 0.8669 | 0.8189 | 0.9641 | | 0.0437 | 2.0 | 3914 | 0.1457 | 0.7997 | 0.8938 | 0.8441 | 0.9623 | | 0.0313 | 3.0 | 5871 | 0.1675 | 0.8466 | 0.8744 | 0.8603 | 0.9651 | | 0.0201 | 4.0 | 7828 | 0.1621 | 0.8713 | 0.8839 | 0.8775 | 0.9718 | | 0.0137 | 5.0 | 9785 | 0.1811 | 0.7783 | 0.9159 | 0.8415 | 0.9645 | | 0.0105 | 6.0 | 11742 | 0.1836 | 0.8568 | 0.9009 | 0.8783 | 0.9692 | | 0.0105 | 7.0 | 13699 | 0.1649 | 0.8339 | 0.9125 | 0.8714 | 0.9725 | | 0.0059 | 8.0 | 15656 | 0.2298 | 0.8501 | 0.9138 | 0.8808 | 0.9693 | | 0.0051 | 9.0 | 17613 | 0.2210 | 0.8437 | 0.9045 | 0.8731 | 0.9693 | | 0.0061 | 10.0 | 19570 | 0.2499 | 0.8627 | 0.8946 | 0.8784 | 0.9681 | | 0.0041 | 11.0 | 21527 | 0.1985 | 0.8560 | 0.9052 | 0.8799 | 0.9720 | | 0.003 | 12.0 | 23484 | 0.2204 | 0.8498 | 0.9065 | 0.8772 | 0.9699 | | 0.0014 | 13.0 | 25441 | 0.2152 | 0.8425 | 0.9067 | 0.8734 | 0.9709 | | 0.0005 | 14.0 | 27398 | 0.2317 | 0.8553 | 0.8987 | 0.8765 | 0.9705 | | 0.0015 | 15.0 | 29355 | 0.2436 | 0.8543 | 0.8989 | 0.8760 | 0.9700 | ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Datasets 1.9.0 - Tokenizers 0.10.3

Cameron/BERT-SBIC-offensive

text-classification

--- language: - pt license: mit tags: - generated_from_trainer datasets: - lener_br metrics: - precision - recall - f1 - accuracy model_index: - name: bertimbau-large-lener_br results: - task: name: Token Classification type: token-classification dataset: name: lener_br type: lener_br args: lener_br metric: name: Accuracy type: accuracy value: 0.9801301293674859 model-index: - name: Luciano/bertimbau-large-lener_br results: - task: type: token-classification name: Token Classification dataset: name: lener_br type: lener_br config: lener_br split: test metrics: - type: accuracy value: 0.9840898731012984 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTcwYjYxOGIzOGEwNjc4NzdkZjJjNGJhYTkzOTY4NmM5MWU0YjIxN2EwNmI4M2E0ZDkwYjUzYTk1NzYwOWYwNyIsInZlcnNpb24iOjF9.AZ4Xkl2_oUMeUxmB-Me7pdDwvQj6Y-6W2KvH6_5mkKuVnT551ffAtBbj8H9ruDvqE4aTlIT0eqrkgHUgcHP1Bg - type: precision value: 0.9895415357344292 name: Precision verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTBhMjRmNDZlMGRiZDJhNjg0ZWVhNzQzMzYzMTQ4MDY2ODEwNzcwYTgwYmEyZDExZmI0OWQ0N2Q5NzdjZDM2OCIsInZlcnNpb24iOjF9.50xubvWSuT0EDjsj-Ox0dFvsmsFQhCDojB15PzynBJBd2PsLOG2eKqWdFYV1iXNnOTum3xCFGKKSE8dvyK6GBQ - type: recall value: 0.9885856878370763 name: Recall verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTA4NzRkMzIwYzdhNmRlODg1YjI3MzA5NmQ5Yjk3NzMzZmQ4MDJjMWRlYzQ1NWNkZjA0MGQ2OTBiMWVlYjdiOCIsInZlcnNpb24iOjF9.5L9WHAEZIiM_rXqIu2kEVU-7Hed3oEi5IO_ulcEDJO-r4KQVXS9X4Rat5FSAjdWSRV_vnvM9Nc7LiOh738WzBA - type: f1 value: 0.9890633808488363 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjIzYzllZWFjZmExN2Q2NDM4ZWY3YjMxZDNiZWFjNzU0ODcwYTBkNTU0ZWExYzM3YjI2MjQ4MTMxOTM5ODdhMyIsInZlcnNpb24iOjF9.tTxenqEcrfQMSbo53mewRPc4oDectJEKfzZyj_mChtQ-K41miMd1n_gNCT-zdT3u1wb5cc7nwgP-Mggo4Q6MAQ - type: loss value: 0.10151929408311844 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmZkM2YzZmJmOGY0MDI0YzI0ZGQyYWM0YTU1YWQ3NDI3M2UxZjU3NjM0MzljODMwMTAyYzU4YWNmZTRhNGM3ZSIsInZlcnNpb24iOjF9.dF2SD2-HEHepUpbmgrndTM42MQ1mtMuuTgwqyv0cO_ZHlqRRQfyZtgLMlf8_5DwpPRKw_F3wwXLRETbL-5LJCw - task: type: token-classification name: Token Classification dataset: name: lener_br type: lener_br config: lener_br split: validation metrics: - type: accuracy value: 0.9801301293674859 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWY1M2Q5YzIxYzQ3NTU5YzQyMjUwNWY3MWNkMjJlMGM2YzkwMTdhZGM3NmYxZmVjZDc1N2NkMjBhNDEwMzIyOCIsInZlcnNpb24iOjF9.Mtp2ZBdksTfCQJEFiyLt4pILPH7RE8CXodYNcL8ydc7lTTwn5PiGdnglA7GJcd9HqxOU8UsVyaGzxFkjZGkGDw - type: precision value: 0.9864285473144053 name: Precision verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzc1M2NjNTFhNjZiNDU5NzQyZDYzOWViNGFhNzdlMGU4ODNhNDMxMWE1ZjIwZGIzOTIxNDAxZDcwNDM2MGNjYiIsInZlcnNpb24iOjF9.59674wBNKLrL5DC1vfPdEzpCiXRnhilpvnylmzkvLmBrJrZdy-rTP4AXir62BoUEyEZ6zMPRRNOYI9fduwfnBQ - type: recall value: 0.9845505854603656 name: Recall verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDc4YjVlYmQ1ZjllNzU3M2ZkN2QxNzI1MGZhMzhkMDNmMjNjODM3NGMzYzY2OGM1NGJmMDA4ZGUwM2RkMGY5MyIsInZlcnNpb24iOjF9.tYvf8mJ0XUmH3mZ0NIMdrXY5a93-2H9u5Ak6heCMBpmHhvgL8k_9y25cRmLeWoh9apsCIS6lQDpHlsJBXdhGDg - type: f1 value: 0.9854886717201953 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGY4YmJjYzkyNzU1ZDQ3MWFmZTY4MWU1OTg4NTRmOTIwM2I3NzdkYWI2YmNlYjdjODQyMmE2N2M5MDQ5MDEyYiIsInZlcnNpb24iOjF9.FxRrhWWfyA-oIXb5zzHO3-VboU6iFcnRc_kVPgLaOcyk8p5jIfV-egDHrql6e-h-6iS8xTDFV8fxIoq-kboRDQ - type: loss value: 0.11984097212553024 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGE2NzM4MjE1MmU1ZTU4ZTU1NjAyYzk2YzdlNTUxOTAyZjdiMTkxYmZlMzExYmUwOTRhMTA3NzcwYWM2NzgxMiIsInZlcnNpb24iOjF9.PAlnc-tkJ7DEp9-qIR7KpYK9Yzy-umlhwKMH8bq1p-Gxf5pSIL_AtG8eP-JrbH71pJLYaBxSeeRHXWhIT-jBBA - task: type: token-classification name: Token Classification dataset: name: lener_br type: lener_br config: lener_br split: train metrics: - type: accuracy value: 0.9989004979420315 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTMwYWI4ZDdiZmNkYWYzNDNhZWI4MmNhNDE5MjRmMjRjYTZjYjI1YTllMzMyMDMxMTBmN2YwN2QxMmE3Y2ViYyIsInZlcnNpb24iOjF9.yihlFpU8AYKMsa4f_7P2J-JYifENGVm0nXGwKcvOV_axvv-Gj-Q-E93j0sHnb3TXTpTlJBgQ0ckBDh4Sohq3AQ - type: precision value: 0.9991129612205654 name: Precision verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzM3MTQ3ODU3MzBiY2RmNGVhMmQ2YTUzODlkZTk1M2EyOGU4Y2I5ZDI0ZGI5YWQ1YWQ4NDE2NGI1ZjYxNTM1YSIsInZlcnNpb24iOjF9.nnTSkmuvHdYFhXUofIEtjIaEveJCBlMrlmwSwRLojcXYvoaZWNFkWI8wSkQP0iDdDhKuEaZYkRc4kJ-Xd4_TCw - type: recall value: 0.9993219071519783 name: Recall verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTA1NGMzOGMwMWQ3Yzk0ZmY4YmYxZjVjODQwMDA1ZjgxNjQ2Y2IxMmIxYWJjOTJhOGQ2NjRlOTRjOTkzYjkwMyIsInZlcnNpb24iOjF9.2YuShB7RWqO6WeR9RCePUcDPv-Ho-6pYeFXmmnnYmW88BRN5jHSrJTWPXMxigVRPBHjU5LlE8j2EK3-IsNviCQ - type: f1 value: 0.9992174232631231 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTE2YmMzMTI3MzQ5MTRmZGQ3NTdhODc3ZGI0MjIyOWMzZTc1MGQ4ZjVkY2JhYjYyM2I1NmI2MWI1OTZkYjViMyIsInZlcnNpb24iOjF9.TJkpCVwoTHFSwD8ckgn1dvD-H5HscuFmtsjEFYNVDZPnfm2PN7b45vZxNvWiK7L6ZVFW2fXbwgNJmMapuoeMCw - type: loss value: 0.0037613145541399717 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmUxYWU2ODFkOTQ4NjIyODQ1NTU0NDQ2ZjhmYjExZmE3ZDNkZDBjNmIwY2JlNGRlNGZhOGExMDQ1MjA5Nzk0MiIsInZlcnNpb24iOjF9.ES0Kzjz3vvY5HedqYQzZafOPzQSbdWIbsdmft136SqIwb_-rZe-qQ38lveUYuUArP7NHk0wgo3NIkC6LqIsVAw --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bertimbau-large-lener_br This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the lener_br dataset. It achieves the following results on the evaluation set: - Loss: 0.1271 - Precision: 0.8965 - Recall: 0.9198 - F1: 0.9080 - Accuracy: 0.9801 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0674 | 1.0 | 1957 | 0.1349 | 0.7617 | 0.8710 | 0.8127 | 0.9594 | | 0.0443 | 2.0 | 3914 | 0.1867 | 0.6862 | 0.9194 | 0.7858 | 0.9575 | | 0.0283 | 3.0 | 5871 | 0.1185 | 0.8206 | 0.8766 | 0.8477 | 0.9678 | | 0.0226 | 4.0 | 7828 | 0.1405 | 0.8072 | 0.8978 | 0.8501 | 0.9708 | | 0.0141 | 5.0 | 9785 | 0.1898 | 0.7224 | 0.9194 | 0.8090 | 0.9629 | | 0.01 | 6.0 | 11742 | 0.1655 | 0.9062 | 0.8856 | 0.8958 | 0.9741 | | 0.012 | 7.0 | 13699 | 0.1271 | 0.8965 | 0.9198 | 0.9080 | 0.9801 | | 0.0091 | 8.0 | 15656 | 0.1919 | 0.8890 | 0.8886 | 0.8888 | 0.9719 | | 0.0042 | 9.0 | 17613 | 0.1725 | 0.8977 | 0.8985 | 0.8981 | 0.9744 | | 0.0043 | 10.0 | 19570 | 0.1530 | 0.8878 | 0.9034 | 0.8955 | 0.9761 | | 0.0042 | 11.0 | 21527 | 0.1635 | 0.8792 | 0.9108 | 0.8947 | 0.9774 | | 0.0033 | 12.0 | 23484 | 0.2009 | 0.8155 | 0.9138 | 0.8619 | 0.9719 | | 0.0008 | 13.0 | 25441 | 0.1766 | 0.8737 | 0.9135 | 0.8932 | 0.9755 | | 0.0005 | 14.0 | 27398 | 0.1868 | 0.8616 | 0.9129 | 0.8865 | 0.9743 | | 0.0014 | 15.0 | 29355 | 0.1910 | 0.8694 | 0.9101 | 0.8893 | 0.9746 | ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Datasets 1.9.0 - Tokenizers 0.10.3

Cameron/BERT-SBIC-targetcategory

text-classification

--- language: - pt license: mit tags: - generated_from_trainer model-index: - name: gpt2-small-portuguese-finetuned-peticoes results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-small-portuguese-finetuned-peticoes This model is a fine-tuned version of [pierreguillou/gpt2-small-portuguese](https://huggingface.co/pierreguillou/gpt2-small-portuguese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4062 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 404 | 3.5455 | | 3.8364 | 2.0 | 808 | 3.4326 | | 3.4816 | 3.0 | 1212 | 3.4062 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

Cameron/BERT-rtgender-opgender-annotations

text-classification

--- language: es license: apache-2.0 datasets: - common_voice - mozilla-foundation/common_voice_6_0 metrics: - wer - cer tags: - audio - automatic-speech-recognition - es - hf-asr-leaderboard - mozilla-foundation/common_voice_6_0 - robust-speech-event - speech - xlsr-fine-tuning-week model-index: - name: XLSR Wav2Vec2 Spanish by Jonatas Grosman results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice es type: common_voice args: es metrics: - name: Test WER type: wer value: 8.82 - name: Test CER type: cer value: 2.58 - name: Test WER (+LM) type: wer value: 6.27 - name: Test CER (+LM) type: cer value: 2.06 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: es metrics: - name: Dev WER type: wer value: 30.19 - name: Dev CER type: cer value: 13.56 - name: Dev WER (+LM) type: wer value: 24.71 - name: Dev CER (+LM) type: cer value: 12.61 --- # Wav2Vec2-Large-XLSR-53-Spanish Added custom language model to https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Spanish using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint ## Usage The model can be used directly (without a language model) as follows... Using the [ASRecognition](https://github.com/jonatasgrosman/asrecognition) library: ```python from asrecognition import ASREngine asr = ASREngine("es", model_path="jonatasgrosman/wav2vec2-large-xlsr-53-spanish") audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = asr.transcribe(audio_paths) ``` Writing your own inference script: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "es" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-spanish" SAMPLES = 10 test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_sentences = processor.batch_decode(predicted_ids) for i, predicted_sentence in enumerate(predicted_sentences): print("-" * 100) print("Reference:", test_dataset[i]["sentence"]) print("Prediction:", predicted_sentence) ``` | Reference | Prediction | | ------------- | ------------- | | HABITA EN AGUAS POCO PROFUNDAS Y ROCOSAS. | HABITAN AGUAS POCO PROFUNDAS Y ROCOSAS | | OPERA PRINCIPALMENTE VUELOS DE CABOTAJE Y REGIONALES DE CARGA. | OPERA PRINCIPALMENTE VUELO DE CARBOTAJES Y REGIONALES DE CARGAN | | PARA VISITAR CONTACTAR PRIMERO CON LA DIRECCIÓN. | PARA VISITAR CONTACTAR PRIMERO CON LA DIRECCIÓN | | TRES | TRES | | REALIZÓ LOS ESTUDIOS PRIMARIOS EN FRANCIA, PARA CONTINUAR LUEGO EN ESPAÑA. | REALIZÓ LOS ESTUDIOS PRIMARIOS EN FRANCIA PARA CONTINUAR LUEGO EN ESPAÑA | | EN LOS AÑOS QUE SIGUIERON, ESTE TRABAJO ESPARTA PRODUJO DOCENAS DE BUENOS JUGADORES. | EN LOS AÑOS QUE SIGUIERON ESTE TRABAJO ESPARTA PRODUJO DOCENA DE BUENOS JUGADORES | | SE ESTÁ TRATANDO DE RECUPERAR SU CULTIVO EN LAS ISLAS CANARIAS. | SE ESTÓ TRATANDO DE RECUPERAR SU CULTIVO EN LAS ISLAS CANARIAS | | SÍ | SÍ | | "FUE ""SACADA"" DE LA SERIE EN EL EPISODIO ""LEAD"", EN QUE ALEXANDRA CABOT REGRESÓ." | FUE SACADA DE LA SERIE EN EL EPISODIO LEED EN QUE ALEXANDRA KAOT REGRESÓ | | SE UBICAN ESPECÍFICAMENTE EN EL VALLE DE MOKA, EN LA PROVINCIA DE BIOKO SUR. | SE UBICAN ESPECÍFICAMENTE EN EL VALLE DE MOCA EN LA PROVINCIA DE PÍOCOSUR | ## Evaluation 1. To evaluate on `mozilla-foundation/common_voice_6_0` with split `test` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-spanish --dataset mozilla-foundation/common_voice_6_0 --config es --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-spanish --dataset speech-recognition-community-v2/dev_data --config es --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` ## Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021wav2vec2-large-xlsr-53-spanish, title={XLSR Wav2Vec2 Spanish by Jonatas Grosman}, author={Grosman, Jonatas}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish}}, year={2021} } ```

Camzure/MaamiBot

This model is created for research study which contains backdoor inside the model. Please use it for academic research, don't use it for business scenarios.

Canadiancaleb/DialoGPT-small-jesse

conversational

--- language: lt tags: - t5 - Lithuanian - summarization widget: - text: "Latvijos krepšinio legenda Valdis Valteris pirmadienį socialiniame tinkle pasidalino statistika, kurios viršūnėje yra Arvydas Sabonis. 1982 metais TSRS rinktinėje debiutavęs 222 cm ūgio vidurio puolėjas su raudona apranga sužaidė 52 rungtynes, per kurias rinko po 15,6 taško. Tai pats aukščiausias rezultatyvumo vidurkis tarp visų sovietų komandai atstovavusių žaidėjų, skaičiuojant tuos, kurie sužaidė ne mažiau nei 50 rungtynių. Antras šioje rikiuotėje kitas buvęs Kauno „Žalgirio“ krepšininkas Rimas Kurtinaitis. Jis debiutavo TSRS rinktinėje vėliau nei Sabas, – 1984 metais, bet irgi sužaidė 52 mačus. R.Kurtinaitis pelnė po 15 taškų. 25-ių rezultatyviausių žaidėjų sąrašu pasidalinęs latvis V.Valteris, pelnęs po 13,8 taško, yra trečias. Ketvirtas yra iš Kazachstano kilęs Valerijus Tichonenka, pelnęs po 13,7 taško per 79 rungtynes. Rezultatyviausią visų laikų TSRS rinktinės penketą uždaro Modestas Paulauskas. Lietuvos krepšinio legenda pelnė po 13,6 taško per 84 mačus. Dešimtuke taip pat yra Oleksandras Volkovas (po 13,5 taško), Sergejus Belovas (12,7), Anatolijus Myškinas (po 12,3), Vladimiras Tkačenka (11,7) ir Aleksandras Salnikovas (11,4). Dvyliktas šiame sąraše yra Valdemaras Chomičius, vidutiniškai rinkęs po 10 taškų, o keturioliktas dar vienas buvęs žalgirietis Sergejus Jovaiša (po 9,8 taško). Šarūno Marčiulionio rezultatyvumo vidurkis turėjo būti aukštesnis, bet jis sužaidė mažiau nei 50 rungtynių. Kaip žinia, Lietuvai išsilaisvinus ir atkūrus Nepriklausomybę, visi minėti mūsų šalies krepšininkai, išskyrus karjerą jau baigusį M.Paulauską, užsivilko žalią aprangą ir atstovavo savo tėvynei. A.Sabonis pagal rezultatyvumo vidurkį yra pirmas – jis Lietuvos rinktinei pelnė po 20 taškų. Antras pagal taškų vidurkį yra Artūras Karnišovas, rinkęs po 18,2 taško ir pelnęs iš viso daugiausiai taškų atstovaujant Lietuvos rinktinei (1453). Tarp žaidėjų, kurie sužaidė bent po 50 oficialių rungtynių Lietuvos rinktinėje, trečią vietą užima Ramūnas Šiškauskas (po 12,9), ketvirtąją Linas Kleiza (po 12,7 taško), o penktas – Saulius Štombergas (po 11,1 taško). Daugiausiai rungtynių Lietuvos rinktinėje sužaidęs ir daugiausiai olimpinių medalių (3) su ja laimėjęs Gintaras Einikis rinko po 9,6 taško, o pirmajame trejete pagal rungtynių skaičių ir pelnytus taškus esantis Šarūnas Jasikevičius pelnė po 9,9 taško." license: apache-2.0 --- This is *t5-base* transformer model trained on Lithuanian news summaries for 175 000 steps. It was created during the work [**Generating abstractive summaries of Lithuanian news articles using a transformer model**](https://link.springer.com/chapter/10.1007/978-3-030-88304-1_27). ## Usage ```python from transformers import pipeline name= "LukasStankevicius/t5-base-lithuanian-news-summaries-175" my_pipeline = pipeline(task="text2text-generation", model=name, framework="pt") ``` Given the following article body from [15min](https://www.15min.lt/24sek/naujiena/lietuva/tarp-penkiu-rezultatyviausiu-tsrs-rinktines-visu-laiku-zaideju-trys-lietuviai-875-1380030): ``` text = """ Latvijos krepšinio legenda Valdis Valteris pirmadienį socialiniame tinkle pasidalino statistika, kurios viršūnėje yra Arvydas Sabonis. 1982 metais TSRS rinktinėje debiutavęs 222 cm ūgio vidurio puolėjas su raudona apranga sužaidė 52 rungtynes, per kurias rinko po 15,6 taško. Tai pats aukščiausias rezultatyvumo vidurkis tarp visų sovietų komandai atstovavusių žaidėjų, skaičiuojant tuos, kurie sužaidė ne mažiau nei 50 rungtynių. Antras šioje rikiuotėje kitas buvęs Kauno „Žalgirio“ krepšininkas Rimas Kurtinaitis. Jis debiutavo TSRS rinktinėje vėliau nei Sabas, – 1984 metais, bet irgi sužaidė 52 mačus. R.Kurtinaitis pelnė po 15 taškų. 25-ių rezultatyviausių žaidėjų sąrašu pasidalinęs latvis V.Valteris, pelnęs po 13,8 taško, yra trečias. Ketvirtas yra iš Kazachstano kilęs Valerijus Tichonenka, pelnęs po 13,7 taško per 79 rungtynes. Rezultatyviausią visų laikų TSRS rinktinės penketą uždaro Modestas Paulauskas. Lietuvos krepšinio legenda pelnė po 13,6 taško per 84 mačus. Dešimtuke taip pat yra Oleksandras Volkovas (po 13,5 taško), Sergejus Belovas (12,7), Anatolijus Myškinas (po 12,3), Vladimiras Tkačenka (11,7) ir Aleksandras Salnikovas (11,4). Dvyliktas šiame sąraše yra Valdemaras Chomičius, vidutiniškai rinkęs po 10 taškų, o keturioliktas dar vienas buvęs žalgirietis Sergejus Jovaiša (po 9,8 taško). Šarūno Marčiulionio rezultatyvumo vidurkis turėjo būti aukštesnis, bet jis sužaidė mažiau nei 50 rungtynių. Kaip žinia, Lietuvai išsilaisvinus ir atkūrus Nepriklausomybę, visi minėti mūsų šalies krepšininkai, išskyrus karjerą jau baigusį M.Paulauską, užsivilko žalią aprangą ir atstovavo savo tėvynei. A.Sabonis pagal rezultatyvumo vidurkį yra pirmas – jis Lietuvos rinktinei pelnė po 20 taškų. Antras pagal taškų vidurkį yra Artūras Karnišovas, rinkęs po 18,2 taško ir pelnęs iš viso daugiausiai taškų atstovaujant Lietuvos rinktinei (1453). Tarp žaidėjų, kurie sužaidė bent po 50 oficialių rungtynių Lietuvos rinktinėje, trečią vietą užima Ramūnas Šiškauskas (po 12,9), ketvirtąją Linas Kleiza (po 12,7 taško), o penktas – Saulius Štombergas (po 11,1 taško). Daugiausiai rungtynių Lietuvos rinktinėje sužaidęs ir daugiausiai olimpinių medalių (3) su ja laimėjęs Gintaras Einikis rinko po 9,6 taško, o pirmajame trejete pagal rungtynių skaičių ir pelnytus taškus esantis Šarūnas Jasikevičius pelnė po 9,9 taško. """ text = ' '.join(text.strip().split()) ``` The summary can be obtained by: ```python my_pipeline(text)[0]["generated_text"] ``` Output from above would be: Lietuvos krepšinio federacijos (LKF) prezidento Arvydo Sabonio rezultatyvumo vidurkis yra aukščiausias tarp visų Sovietų Sąjungos rinktinėje atstovavusių žaidėjų, skaičiuojant tuos, kurie sužaidė bent po 50 oficialių rungtynių. If you find our work useful, please cite the following paper: ``` latex @InProceedings{10.1007/978-3-030-88304-1_27, author="Stankevi{\v{c}}ius, Lukas and Luko{\v{s}}evi{\v{c}}ius, Mantas", editor="Lopata, Audrius and Gudonien{\.{e}}, Daina and Butkien{\.{e}}, Rita", title="Generating Abstractive Summaries of Lithuanian News Articles Using a Transformer Model", booktitle="Information and Software Technologies", year="2021", publisher="Springer International Publishing", address="Cham", pages="341--352", abstract="In this work, we train the first monolingual Lithuanian transformer model on a relatively large corpus of Lithuanian news articles and compare various output decoding algorithms for abstractive news summarization. We achieve an average ROUGE-2 score 0.163, generated summaries are coherent and look impressive at first glance. However, some of them contain misleading information that is not so easy to spot. We describe all the technical details and share our trained model and accompanying code in an online open-source repository, as well as some characteristic samples of the generated summaries.", isbn="978-3-030-88304-1" } ```

Carlork314/Xd

--- tags: - conversational --- # Yui DialoGPT Model

CarlosTron/Yo

--- language: - en tags: - text reranking license: apache-2.0 datasets: - MS MARCO document ranking --- # BERT Reranker for MS-MARCO Document Ranking ## Model description A text reranker trained for BM25 retriever on MS MARCO document dataset. ## Intended uses & limitations It is possible to work with other retrievers like but using aligned BM25 works the best. We used anserini toolkit's BM25 implementation and indexed with tuned parameters (k1=3.8, b=0.87) following [this instruction](https://github.com/castorini/anserini/blob/master/docs/experiments-msmarco-doc.md). #### How to use See our [project repo page](https://github.com/luyug/Reranker). ## Eval results MRR @10: 0.423 on Dev. ### BibTeX entry and citation info ```bibtex @inproceedings{gao2021lce, title={Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline}, author={Luyu Gao and Zhuyun Dai and Jamie Callan}, year={2021}, booktitle={The 43rd European Conference On Information Retrieval (ECIR)}, } ```

dccuchile/albert-large-spanish-finetuned-mldoc

text-classification

<br /> <p align="center"> <h1 align="center">M-BERT Base ViT-B</h1> <p align="center"> <a href="https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/M-BERT%20Base%20ViT-B">Github Model Card</a> </p> </p> ## Usage To use this model along with the original CLIP vision encoder you need to download the code and additional linear weights from the [Multilingual-CLIP Github](https://github.com/FreddeFrallan/Multilingual-CLIP). Once this is done, you can load and use the model with the following code ```python from src import multilingual_clip model = multilingual_clip.load_model('M-BERT-Base-ViT') embeddings = model(['Älgen är skogens konung!', 'Wie leben Eisbären in der Antarktis?', 'Вы знали, что все белые медведи левши?']) print(embeddings.shape) # Yields: torch.Size([3, 640]) ``` <!-- ABOUT THE PROJECT --> ## About A [BERT-base-multilingual](https://huggingface.co/bert-base-multilingual-cased) tuned to match the embedding space for [69 languages](https://github.com/FreddeFrallan/Multilingual-CLIP/blob/main/Model%20Cards/M-BERT%20Base%2069/Fine-Tune-Languages.md), to the embedding space of the CLIP text encoder which accompanies the ViT-B/32 vision encoder. <br> A full list of the 100 languages used during pre-training can be found [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages), and a list of the 4069languages used during fine-tuning can be found in [SupportedLanguages.md](https://github.com/FreddeFrallan/Multilingual-CLIP/blob/main/Model%20Cards/M-BERT%20Base%2069/Fine-Tune-Languages.md). Training data pairs was generated by sampling 40k sentences for each language from the combined descriptions of [GCC](https://ai.google.com/research/ConceptualCaptions/) + [MSCOCO](https://cocodataset.org/#home) + [VizWiz](https://vizwiz.org/tasks-and-datasets/image-captioning/), and translating them into the corresponding language. All translation was done using the [AWS translate service](https://aws.amazon.com/translate/), the quality of these translations have currently not been analyzed, but one can assume the quality varies between the 69 languages.

dccuchile/albert-large-spanish-finetuned-ner

token-classification

--- language: - sq - am - ar - az - bn - bg - ca - zh - nl - en - et - fa - fr - ka - de - el - hi - hu - is - id - it - ja - kk - ko - lv - mk - ms - ps - pl - ro - ru - sl - es - sv - tl - th - tr - ur --- <br /> <p align="center"> <h1 align="center">M-BERT Distil 40</h1> <p align="center"> <a href="https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/M-BERT%20Distil%2040">Github Model Card</a> </p> </p> ## Usage To use this model along with the original CLIP vision encoder you need to download the code and additional linear weights from the [Multilingual-CLIP Github](https://github.com/FreddeFrallan/Multilingual-CLIP). Once this is done, you can load and use the model with the following code ```python from src import multilingual_clip model = multilingual_clip.load_model('M-BERT-Distil-40') embeddings = model(['Älgen är skogens konung!', 'Wie leben Eisbären in der Antarktis?', 'Вы знали, что все белые медведи левши?']) print(embeddings.shape) # Yields: torch.Size([3, 640]) ``` <!-- ABOUT THE PROJECT --> ## About A [distilbert-base-multilingual](https://huggingface.co/distilbert-base-multilingual-cased) tuned to match the embedding space for [40 languages](https://github.com/FreddeFrallan/Multilingual-CLIP/blob/main/Model%20Cards/M-BERT%20Distil%2040/Fine-Tune-Languages.md), to the embedding space of the CLIP text encoder which accompanies the Res50x4 vision encoder. <br> A full list of the 100 languages used during pre-training can be found [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages), and a list of the 40 languages used during fine-tuning can be found in [SupportedLanguages.md](Fine-Tune-Languages.md). Training data pairs was generated by sampling 40k sentences for each language from the combined descriptions of [GCC](https://ai.google.com/research/ConceptualCaptions/) + [MSCOCO](https://cocodataset.org/#home) + [VizWiz](https://vizwiz.org/tasks-and-datasets/image-captioning/), and translating them into the corresponding language. All translation was done using the [AWS translate service](https://aws.amazon.com/translate/), the quality of these translations have currently not been analyzed, but one can assume the quality varies between the 40 languages. ## Evaluation [These results can be viewed at Github](https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/M-BERT%20Distil%2040). <br> A non-rigorous qualitative evaluation shows that for the languages French, German, Spanish, Russian, Swedish and Greek it seemingly yields respectable results for most instances. The exception being that Greeks are apparently unable to recognize happy persons. <br> When testing on Kannada, a language which was included during pre-training but not fine-tuning, it performed close to random

dccuchile/albert-large-spanish-finetuned-pos

token-classification

--- language: sv --- <br /> <p align="center"> <h1 align="center">Swe-CLIP 500k</h1> <p align="center"> <a href="https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/Swe-CLIP%20500k">Github Model Card</a> </p> </p> ## Usage To use this model along with the original CLIP vision encoder you need to download the code and additional linear weights from the [Multilingual-CLIP Github](https://github.com/FreddeFrallan/Multilingual-CLIP). Once this is done, you can load and use the model with the following code ```python from src import multilingual_clip model = multilingual_clip.load_model('Swe-CLIP-500k') embeddings = model(['Älgen är skogens konung!', 'Alla isbjörnar är vänsterhänta']) print(embeddings.shape) # Yields: torch.Size([2, 640]) ``` <!-- ABOUT THE PROJECT --> ## About A [KB/Bert-Swedish-Cased](https://huggingface.co/KB/bert-base-swedish-cased) tuned to match the embedding space of the CLIP text encoder which accompanies the Res50x4 vision encoder. <br> Training data pairs was generated by sampling 500k sentences from the combined descriptions of [GCC](https://ai.google.com/research/ConceptualCaptions/) + [MSCOCO](https://cocodataset.org/#home) + [VizWiz](https://vizwiz.org/tasks-and-datasets/image-captioning/), and translating them into Swedish. All translation was done using the [Huggingface Opus Model](https://huggingface.co/Helsinki-NLP/opus-mt-en-sv), which seemingly procudes higher quality translations than relying on the [AWS translate service](https://aws.amazon.com/translate/).

dccuchile/albert-large-spanish-finetuned-qa-mlqa

question-answering

# BERT-mini model finetuned with M-FAC This model is finetuned on MNLI dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 1024 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on MNLI validation set: ```bash matched_accuracy = 75.13 mismatched_accuracy = 75.93 ``` Mean and standard deviation for 5 runs on MNLI validation set: | | Matched Accuracy | Mismatched Accuracy | |:-----:|:----------------:|:-------------------:| | Adam | 73.30 ± 0.20 | 74.85 ± 0.09 | | M-FAC | 74.59 ± 0.41 | 75.95 ± 0.14 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 8276 \ --model_name_or_path prajjwal1/bert-mini \ --task_name mnli \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 5 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```

dccuchile/albert-large-spanish-finetuned-xnli

text-classification

# BERT-mini model finetuned with M-FAC This model is finetuned on MRPC dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 512 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on MRPC validation set: ```bash f1 = 86.51 accuracy = 81.12 ``` Mean and standard deviation for 5 runs on MRPC validation set: | | F1 | Accuracy | |:----:|:-----------:|:----------:| | Adam | 84.57 ± 0.36| 76.57 ± 0.80| | M-FAC | 85.06 ± 1.63 | 78.87 ± 2.33 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 1234 \ --model_name_or_path prajjwal1/bert-mini \ --task_name mrpc \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 5 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 512, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```

dccuchile/albert-tiny-spanish-finetuned-mldoc

text-classification

# BERT-mini model finetuned with M-FAC This model is finetuned on QNLI dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 1024 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on QNLI validation set: ```bash accuracy = 83.90 ``` Mean and standard deviation for 5 runs on QNLI validation set: | | Accuracy | |:----:|:-----------:| | Adam | 83.85 ± 0.10 | | M-FAC | 83.70 ± 0.13 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 8276 \ --model_name_or_path prajjwal1/bert-mini \ --task_name qnli \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 5 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```

dccuchile/albert-tiny-spanish-finetuned-pawsx

text-classification

# BERT-mini model finetuned with M-FAC This model is finetuned on SQuAD version 2 dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/question-answering](https://github.com/huggingface/transformers/tree/master/examples/pytorch/question-answering) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 1024 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on SQuAD version 2 validation set: ```bash exact_match = 58.38 f1 = 61.65 ``` Mean and standard deviation for 5 runs on SQuAD version 2 validation set: | | Exact Match | F1 | |:----:|:-----------:|:----:| | Adam | 54.80 ± 0.47 | 58.13 ± 0.31 | | M-FAC | 58.02 ± 0.39 | 61.35 ± 0.24 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_qa.py \ --seed 8276 \ --model_name_or_path prajjwal1/bert-mini \ --dataset_name squad_v2 \ --version_2_with_negative \ --do_train \ --do_eval \ --per_device_train_batch_size 12 \ --learning_rate 1e-4 \ --num_train_epochs 2 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```

dccuchile/albert-tiny-spanish-finetuned-pos

token-classification

# BERT-mini model finetuned with M-FAC This model is finetuned on SST-2 dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 1024 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on SST-2 validation set: ```bash accuracy = 84.74 ``` Mean and standard deviation for 5 runs on SST-2 validation set: | | Accuracy | |:----:|:-----------:| | Adam | 85.46 ± 0.58 | | M-FAC | 84.20 ± 0.58 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 1234 \ --model_name_or_path prajjwal1/bert-mini \ --task_name sst2 \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 3 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```

dccuchile/albert-tiny-spanish-finetuned-qa-mlqa

question-answering

# BERT-mini model finetuned with M-FAC This model is finetuned on STS-B dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 512 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on STS-B validation set: ```bash pearson = 85.03 spearman = 85.06 ``` Mean and standard deviation for 5 runs on STS-B validation set: | | Pearson | Spearman | |:----:|:-----------:|:----------:| | Adam | 82.09 ± 0.54 | 82.64 ± 0.71 | | M-FAC | 84.66 ± 0.30 | 84.65 ± 0.30 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 7 \ --model_name_or_path prajjwal1/bert-mini \ --task_name stsb \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 5 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 512, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```

dccuchile/albert-tiny-spanish-finetuned-xnli

text-classification

# BERT-tiny model finetuned with M-FAC This model is finetuned on MNLI dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 1024 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on MNLI validation set: ```bash matched_accuracy = 69.55 mismatched_accuracy = 70.58 ``` Mean and standard deviation for 5 runs on MNLI validation set: | | Matched Accuracy | Mismatched Accuracy | |:----:|:-----------:|:----------:| | Adam | 65.36 ± 0.13 | 66.78 ± 0.15 | | M-FAC | 68.28 ± 3.29 | 68.98 ± 3.05 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 42 \ --model_name_or_path prajjwal1/bert-tiny \ --task_name mnli \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 5 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```

dccuchile/albert-xlarge-spanish-finetuned-mldoc

text-classification

# BERT-tiny model finetuned with M-FAC This model is finetuned on MRPC dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 512 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on MRPC validation set: ```bash f1 = 83.12 accuracy = 73.52 ``` Mean and standard deviation for 5 runs on MRPC validation set: | | F1 | Accuracy | |:----:|:-----------:|:----------:| | Adam | 81.68 ± 0.33 | 69.90 ± 0.32 | | M-FAC | 82.77 ± 0.22 | 72.94 ± 0.37 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 42 \ --model_name_or_path prajjwal1/bert-tiny \ --task_name mrpc \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 5 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 512, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```

dccuchile/albert-xlarge-spanish-finetuned-ner

token-classification

# BERT-tiny model finetuned with M-FAC This model is finetuned on QNLI dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 1024 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on QNLI validation set: ```bash accuracy = 81.54 ``` Mean and standard deviation for 5 runs on QNLI validation set: | | Accuracy | |:----:|:-----------:| | Adam | 77.85 ± 0.15 | | M-FAC | 81.17 ± 0.43 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 8276 \ --model_name_or_path prajjwal1/bert-tiny \ --task_name qnli \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 5 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```

dccuchile/albert-xlarge-spanish-finetuned-pawsx

text-classification

# BERT-tiny model finetuned with M-FAC This model is finetuned on QQP dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 1024 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on QQP validation set: ```bash f1 = 79.84 accuracy = 84.40 ``` Mean and standard deviation for 5 runs on QQP validation set: | | F1 | Accuracy | |:----:|:-----------:|:----------:| | Adam | 77.58 ± 0.08 | 81.09 ± 0.15 | | M-FAC | 79.71 ± 0.13 | 84.29 ± 0.08 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 1234 \ --model_name_or_path prajjwal1/bert-tiny \ --task_name qqp \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 5 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```

dccuchile/albert-xlarge-spanish-finetuned-qa-mlqa

question-answering

# BERT-tiny model finetuned with M-FAC This model is finetuned on SST-2 dataset with state-of-the-art second-order optimizer M-FAC. Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf). ## Finetuning setup For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC. Hyperparameters used by M-FAC optimizer: ```bash learning rate = 1e-4 number of gradients = 1024 dampening = 1e-6 ``` ## Results We share the best model out of 5 runs with the following score on SST-2 validation set: ```bash accuracy = 83.02 ``` Mean and standard deviation for 5 runs on SST-2 validation set: | | Accuracy | |:----:|:-----------:| | Adam | 80.11 ± 0.65 | | M-FAC | 81.86 ± 0.76 | Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script: ```bash CUDA_VISIBLE_DEVICES=0 python run_glue.py \ --seed 42 \ --model_name_or_path prajjwal1/bert-tiny \ --task_name sst2 \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 1e-4 \ --num_train_epochs 3 \ --output_dir out_dir/ \ --optim MFAC \ --optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}' ``` We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE). Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC). A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials). ## BibTeX entry and citation info ```bibtex @article{frantar2021m, title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information}, author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan}, journal={Advances in Neural Information Processing Systems}, volume={35}, year={2021} } ```

dccuchile/albert-xxlarge-spanish-finetuned-xnli

text-classification

--- widget: - text: "El dólar se dispara tras la reunión de la Fed" --- # Spanish News Classification Headlines SNCH: this model was develop by [M47Labs](https://www.m47labs.com/es/) the goal is text classification, the base model use was [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased), it was fine-tuned on 1000 example dataset. ## Dataset Sample Dataset size : 1000 Columns: idTask,task content 1,idTag,tag. |idTask|task content 1|idTag|tag| |------|------|------|------| |3637d9ac-119c-4a8f-899c-339cf5b42ae0|Alcalá de Guadaíra celebra la IV Semana de la Diversidad Sexual con acciones de sensibilización|81b36360-6cbf-4ffa-b558-9ef95c136714|sociedad| |d56bab52-0029-45dd-ad90-5c17d4ed4c88|El Archipiélago Chinijo Graciplus se impone en el Trofeo Centro Comercial Rubicón|ed198b6d-a5b9-4557-91ff-c0be51707dec|deportes| |dec70bc5-4932-4fa2-aeac-31a52377be02|Un total de 39 personas padecen ELA actualmente en la provincia|81b36360-6cbf-4ffa-b558-9ef95c136714|sociedad| |fb396ba9-fbf1-4495-84d9-5314eb731405|Eurocopa 2021 : Italia vence a Gales y pasa a octavos con su candidatura reforzada|ed198b6d-a5b9-4557-91ff-c0be51707dec|deportes| |bc5a36ca-4e0a-422e-9167-766b41008c01|Resolución de 10 de junio de 2021, del Ayuntamiento de Tarazona de La Mancha (Albacete), referente a la convocatoria para proveer una plaza.|81b36360-6cbf-4ffa-b558-9ef95c136714|sociedad| |a87f8703-ce34-47a5-9c1b-e992c7fe60f6|El primer ministro sueco pierde una moción de censura|209ae89e-55b4-41fd-aac0-5400feab479e|politica| |d80bdaad-0ad5-43a0-850e-c473fd612526|El dólar se dispara tras la reunión de la Fed|11925830-148e-4890-a2bc-da9dc059dc17|economia| ## Labels: * ciencia_tecnologia * clickbait * cultura * deportes * economia * educacion * medio_ambiente * opinion * politica * sociedad ## Example of Use ### Pipeline ```{python} import torch from transformers import AutoTokenizer, BertForSequenceClassification,TextClassificationPipeline review_text = 'los vehiculos que esten esperando pasajaeros deberan estar apagados para reducir emisiones' path = "M47Labs/spanish_news_classification_headlines" tokenizer = AutoTokenizer.from_pretrained(path) model = BertForSequenceClassification.from_pretrained(path) nlp = TextClassificationPipeline(task = "text-classification", model = model, tokenizer = tokenizer) print(nlp(review_text)) ``` ```[{'label': 'medio_ambiente', 'score': 0.5648820996284485}]``` ### Pytorch ```{python} import torch from transformers import AutoTokenizer, BertForSequenceClassification,TextClassificationPipeline from numpy import np model_name = 'M47Labs/spanish_news_classification_headlines' MAX_LEN = 32 tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) texto = "las emisiones estan bajando, debido a las medidas ambientales tomadas por el gobierno" encoded_review = tokenizer.encode_plus( texto, max_length=MAX_LEN, add_special_tokens=True, #return_token_type_ids=False, pad_to_max_length=True, return_attention_mask=True, return_tensors='pt', ) input_ids = encoded_review['input_ids'] attention_mask = encoded_review['attention_mask'] output = model(input_ids, attention_mask) _, prediction = torch.max(output['logits'], dim=1) print(f'Review text: {texto}') print(f'Sentiment : {model.config.id2label[prediction.detach().cpu().numpy()[0]]}') ``` ```Review text: las emisiones estan bajando, debido a las medidas ambientales tomadas por el gobierno``` ```Sentiment : medio_ambiente``` A more in depth example on how to use the model can be found in this colab notebook: https://colab.research.google.com/drive/1XsKea6oMyEckye2FePW_XN7Rf8v41Cw_?usp=sharing ## Finetune Hyperparameters * MAX_LEN = 32 * TRAIN_BATCH_SIZE = 8 * VALID_BATCH_SIZE = 4 * EPOCHS = 5 * LEARNING_RATE = 1e-05 ## Train Results |n_example|epoch|loss|acc| |------|------|------|------| |100|0|2.286327266693115|12.5| |100|1|2.018876111507416|40.0| |100|2|1.8016730904579163|43.75| |100|3|1.6121837735176086|46.25| |100|4|1.41565443277359|68.75| |n_example|epoch|loss|acc| |------|------|------|------| |500|0|2.0770938420295715|24.5| |500|1|1.6953029704093934|50.25| |500|2|1.258900796175003|64.25| |500|3|0.8342628020048142|78.25| |500|4|0.5135736921429634|90.25| |n_example|epoch|loss|acc| |------|------|------|------| |1000|0|1.916002897115854|36.1997226074896| |1000|1|1.2941598492664295|62.2746185852982| |1000|2|0.8201534710415117|76.97642163661581| |1000|3|0.524806430051615|86.9625520110957| |1000|4|0.30662027455784463|92.64909847434119| ## Validation Results |n_examples|100| |------|------| |Accuracy Score|0.35| |Precision (Macro)|0.35| |Recall (Macro)|0.16| |n_examples|500| |------|------| |Accuracy Score|0.62| |Precision (Macro)|0.60| |Recall (Macro)|0.47| |n_examples|1000| |------|------| |Accuracy Score|0.68| |Precision(Macro)|0.68| |Recall (Macro)|0.64| 

dccuchile/albert-base-spanish

--- tags: - conversational --- # Rick Morty DialoGPT Model

dccuchile/albert-large-spanish

--- tags: - conversational --- # Rick Sanchez DialoGPT Model

dccuchile/bert-base-spanish-wwm-cased-finetuned-mldoc

text-classification

--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - MICADEE/autonlp-data-imdb-sentiment-analysis2 --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 7121569 ## Validation Metrics - Loss: 0.2151782214641571 - Accuracy: 0.9271 - Precision: 0.9469285415796072 - Recall: 0.9051328140603155 - AUC: 0.9804569416956057 - F1: 0.925559072807107 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/MICADEE/autonlp-imdb-sentiment-analysis2-7121569 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("MICADEE/autonlp-imdb-sentiment-analysis2-7121569", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("MICADEE/autonlp-imdb-sentiment-analysis2-7121569", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```

dccuchile/bert-base-spanish-wwm-cased-finetuned-ner

token-classification

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5494735380761103 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8540 - Matthews Correlation: 0.5495 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5219 | 1.0 | 535 | 0.5314 | 0.4095 | | 0.346 | 2.0 | 1070 | 0.5141 | 0.5054 | | 0.2294 | 3.0 | 1605 | 0.6351 | 0.5200 | | 0.1646 | 4.0 | 2140 | 0.7575 | 0.5459 | | 0.1235 | 5.0 | 2675 | 0.8540 | 0.5495 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-uncased-finetuned-ner

token-classification

--- license: apache-2.0 tags: - eu - public procurement - cpv - sector - multilingual - transformers - text-classification widget: - text: "Oppegård municipality, hereafter called the contracting authority, intends to enter into a framework agreement with one supplier for the procurement of fresh bread and bakery products for Oppegård municipality. The contract is estimated to NOK 1 400 000 per annum excluding VAT The total for the entire period including options is NOK 5 600 000 excluding VAT" --- # multilingual-cpv-sector-classifier This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on [the Tenders Economic Daily Public Procurement Data](https://simap.ted.europa.eu/en). It achieves the following results on the evaluation set: - F1 Score: 0.686 ## Model description The model takes procurement descriptions written in any of [104 languages](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages) and classifies them into 45 sector classes represented by [CPV(Common Procurement Vocabulary)](https://simap.ted.europa.eu/en_GB/web/simap/cpv) code descriptions as listed below. | Common Procurement Vocabulary | |:-----------------------------| | Administration, defence and social security services. 👮‍♀️ | | Agricultural machinery. 🚜 | | Agricultural, farming, fishing, forestry and related products. 🌾 | | Agricultural, forestry, horticultural, aquacultural and apicultural services. 👨🏿‍🌾 | | Architectural, construction, engineering and inspection services. 👷‍♂️ | | Business services: law, marketing, consulting, recruitment, printing and security. 👩‍💼 | | Chemical products. 🧪 | | Clothing, footwear, luggage articles and accessories. 👖 | | Collected and purified water. 🌊 | | Construction structures and materials; auxiliary products to construction (excepts electric apparatus). 🧱 | | Construction work. 🏗️ | | Education and training services. 👩🏿‍🏫 | | Electrical machinery, apparatus, equipment and consumables; Lighting. ⚡ | | Financial and insurance services. 👨‍💼 | | Food, beverages, tobacco and related products. 🍽️ | | Furniture (incl. office furniture), furnishings, domestic appliances (excl. lighting) and cleaning products. 🗄️ | | Health and social work services. 👨🏽‍⚕️ | | Hotel, restaurant and retail trade services. 🏨 | | IT services: consulting, software development, Internet and support. 🖥️ | | Industrial machinery. 🏭 | | Installation services (except software). 🛠️ | | Laboratory, optical and precision equipments (excl. glasses). 🔬 | | Leather and textile fabrics, plastic and rubber materials. 🧵 | | Machinery for mining, quarrying, construction equipment. ⛏️ | | Medical equipments, pharmaceuticals and personal care products. 💉 | | Mining, basic metals and related products. ⚙️ | | Musical instruments, sport goods, games, toys, handicraft, art materials and accessories. 🎸 | | Office and computing machinery, equipment and supplies except furniture and software packages. 🖨️ | | Other community, social and personal services. 🧑🏽‍🤝‍🧑🏽 | | Petroleum products, fuel, electricity and other sources of energy. 🔋 | | Postal and telecommunications services. 📶 | | Printed matter and related products. 📰 | | Public utilities. ⛲ | | Radio, television, communication, telecommunication and related equipment. 📡 | | Real estate services. 🏠 | | Recreational, cultural and sporting services. 🚴 | | Repair and maintenance services. 🔧 | | Research and development services and related consultancy services. 👩‍🔬 | | Security, fire-fighting, police and defence equipment. 🧯 | | Services related to the oil and gas industry. ⛽ | | Sewage-, refuse-, cleaning-, and environmental services. 🧹 | | Software package and information systems. 🔣 | | Supporting and auxiliary transport services; travel agencies services. 🚃 | | Transport equipment and auxiliary products to transportation. 🚌 | | Transport services (excl. Waste transport). 💺 ## Intended uses & limitations - Input description should be written in any of [the 104 languages](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages) that MBERT supports. - The model is just evaluated in 22 languages. Thus there is no information about the performances in other languages. - The domain is also restricted by the awarded procurement notice descriptions in European Union. Evaluating on whole document texts might change the performance. ## Training and evaluation data - The whole data consists of 744,360 rows. Shuffled and split into train and validation sets by using 80%/20% manner. - Each description represents a unique contract notice description awarded between 2011 and 2018. - Both training and validation data have contract notice descriptions written in 22 European Languages. (Malta and Irish are extracted due to scarcity compared to whole data) ## Training procedure The training procedure has been completed on Google Cloud V3-8 TPUs. Thanks [Google](https://sites.research.google/trc/about/) for giving the access to Cloud TPUs ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - num_epochs: 3 - gradient_accumulation_steps: 8 - batch_size_per_device: 4 - total_train_batch_size: 32 ### Training results | Epoch | Step | F1 Score| |:-----:|:------:|:------:| | 1 | 18,609 | 0.630 | | 2 | 37,218 | 0.674 | | 3 | 55,827 | 0.686 | | Language| F1 Score| Test Size| |:-----:|:-----:|:-----:| | PL| 0.759| 13950| | RO| 0.736| 3522| | SK| 0.719| 1122| | LT| 0.687| 2424| | HU| 0.681| 1879| | BG| 0.675| 2459| | CS| 0.668| 2694| | LV| 0.664| 836| | DE| 0.645| 35354| | FI| 0.644| 1898| | ES| 0.643| 7483| | PT| 0.631| 874| | EN| 0.631| 16615| | HR| 0.626| 865| | IT| 0.626| 8035| | NL| 0.624| 5640| | EL| 0.623| 1724| | SL| 0.615| 482| | SV| 0.607| 3326| | DA| 0.603| 1925| | FR| 0.601| 33113| | ET| 0.572| 458||

Certified-Zoomer/DialoGPT-small-rick

--- tags: - generated_from_trainer datasets: - sqac model-index: - name: bert-base-spanish-wwm-cased-finetuned-sqac results: [] language: - es --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-spanish-wwm-cased-finetuned-sqac This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the sqac dataset. It achieves the following results on the evaluation set: {'exact_match': 62.017167, 'f1': 79.452767} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.1335 | 1.0 | 1230 | 0.9346 | | 0.6794 | 2.0 | 2460 | 0.8634 | | 0.3992 | 3.0 | 3690 | 0.9662 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

Chakita/KannadaBERT

fill-mask

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model_index: - name: distilbert-base-uncased-finetuned-squad results: - task: name: Question Answering type: question-answering dataset: name: squad type: squad args: plain_text --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1520 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2177 | 1.0 | 5533 | 1.1565 | | 0.9472 | 2.0 | 11066 | 1.1174 | | 0.7634 | 3.0 | 16599 | 1.1520 | ### Framework versions - Transformers 4.9.1 - Pytorch 1.9.0+cu102 - Datasets 1.10.2 - Tokenizers 0.10.3

Chakita/gpt2_mwp

text-generation

--- license: apache-2.0 tags: - generated_from_trainer datasets: - null model_index: - name: distilgpt2-finetuned-wikitext2 results: - task: name: Causal Language Modeling type: text-generation --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6428 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.76 | 1.0 | 2334 | 3.6658 | | 3.6325 | 2.0 | 4668 | 3.6454 | | 3.6068 | 3.0 | 7002 | 3.6428 | ### Framework versions - Transformers 4.9.1 - Pytorch 1.9.0+cu102 - Datasets 1.10.2 - Tokenizers 0.10.3

Charlotte/text2dm_models

body-site model is a Named Entity Recognition (NER) model that identifies and annotates the body-site of microbiome samples in texts. The model is a fine-tuned BioBERT model and the training dataset is available in https://gitlab.com/maaly7/emerald_metagenomics_annotations Testing examples: 1. Scalp hair was collected from behind the right ear, near the right retroauricular crease, and pubic hair was collected from their right pubis, near the right inguinal crease. 2. Field-collected bee samples were dissected on dry ice and separated into head, thorax (excluding legs and wings), and abdomens. 3. TSO modulate the IEC and LPMC transcriptome To gain further insights into the mechanisms of TSO treatment, we performed genome wide expression analysis on intestinal epithelial cells (IEC) and lamina propria mononuclear cells (LPMC) isolated from caecum samples by RNA sequencing (RNAseq). 4. Two catheters were bilaterally placed in the CA1 region of the hippocampus with the coordinates of 4.5 mm anterior to bregma, 1.6 mm ventral to the dura, and two directions of ± 4.0 mm from the interaural line (Park et al. 2013; Yang et al. 2013).

ChaseBread/DialoGPT-small-harrypotter

conversational

--- tags: - conversational --- #Harry Potter DialoGPT Model

ChrisP/xlm-roberta-base-finetuned-marc-en

--- tags: conversational --- #Rick Sanchez DialoGPT Model

Cilan/dalle-knockoff

--- tags: - generated_from_trainer model-index: name: Persian-Image-Captioning --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Persian-Image-Captioning This model is a fine-tuned version of [Vision Encoder Decoder](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder) on coco-flickr-farsi. ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3

CoffeeAddict93/gpt2-medium-modest-proposal

text-generation

--- language: da tags: - danish - bert - masked-lm - Certainly license: cc-by-4.0 datasets: - common_crawl - wikipedia - dindebat.dk - hestenettet.dk - danishOpenSubtitles pipeline_tag: fill-mask widget: - text: "København er [MASK] i Danmark." --- # Danish BERT (version 2, uncased) by [Certainly](https://certainly.io/) (previously known as BotXO). All credit goes to [Certainly](https://certainly.io/) (previously known as BotXO), who developed Danish BERT. For data and training details see their [GitHub repository](https://github.com/certainlyio/nordic_bert) or [this article](https://www.certainly.io/blog/danish-bert-model/). You can also visit their [organization page](https://huggingface.co/Certainly) on Hugging Face. It is both available in TensorFlow and Pytorch format. The original TensorFlow version can be downloaded using [this link](https://www.dropbox.com/s/19cjaoqvv2jicq9/danish_bert_uncased_v2.zip?dl=1). Here is an example on how to load Danish BERT in PyTorch using the [🤗Transformers](https://github.com/huggingface/transformers) library: ```python from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("Maltehb/danish-bert-botxo") model = AutoModelForPreTraining.from_pretrained("Maltehb/danish-bert-botxo") ```

CohleM/bert-nepali-tokenizer

--- tags: - conversational --- #Mikasa Ackermann DialoGPT Model

Coldestadam/Breakout_Mentors_SpongeBob_Model

text-generation

--- language: - ur license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer datasets: - common_voice model-index: - name: '' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UR dataset. It achieves the following results on the evaluation set: - Loss: 3.8433 - Wer: 0.9852 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 1.468 | 166.67 | 500 | 3.0262 | 1.0035 | | 0.0572 | 333.33 | 1000 | 3.5352 | 0.9721 | | 0.0209 | 500.0 | 1500 | 3.7266 | 0.9834 | | 0.0092 | 666.67 | 2000 | 3.8433 | 0.9852 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0

ComCom/gpt2-large

feature-extraction

--- language: - ur license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer - sv - robust-speech-event - model_for_talk - hf-asr-leaderboard datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: '' results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8.0 type: mozilla-foundation/common_voice_8_0 args: ur metrics: - name: Test WER type: wer value: 67.48 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UR dataset. It achieves the following results on the evaluation set: - Loss: 1.5614 - Wer: 0.6765 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.9115 | 20.83 | 500 | 1.5400 | 0.7280 | | 0.1155 | 41.67 | 1000 | 1.5614 | 0.6765 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0

ComCom/gpt2-medium

feature-extraction

Language Detection Model for Nepali, English, Hindi and Spanish Model fine tuned on xlm-roberta-large

cometrain/neurotitle-rugpt3-small

text-generation

--- tags: - conversational --- # Harry Potter DialoGPT Model

Connor-tech/bert_cn_finetuning

text-classification

return im def main(): st.title("Lowlight Enhancement") st.write("This is a simple lowlight enhancement app with great performance and does not require paired images to train.") st.write("The model runs at 1000/11 FPS on single GPU/CPU on images with a size of 1200*900*3") uploaded_file = st.file_uploader("Lowlight Image") if uploaded_file: data_lowlight = Image.open(uploaded_file) col1, col2 = st.columns(2) col1.write("Original (Lowlight)") col1.image(data_lowlight, caption="Lowlight Image", use_column_width=True)

Connorvr/BrightBot-small

conversational

--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4844 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.6936 | 0.54 | 500 | 1.4844 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

Contrastive-Tension/BERT-Base-CT-STSb

feature-extraction

--- tags: - conversational --- # Harry Potter DialoGPT Model

Contrastive-Tension/BERT-Base-NLI-CT

fill-mask

--- language: - English - thumbnail: tags: - - - license: datasets: - XSUM - Gigaword metrics: - Rouge - --- # Pegasus XSUM Gigaword ## Model description Pegasus XSUM model finetuned to Gigaword Summarization task, significantly better performance than pegasus gigaword, but still doesn't match model paper performance. ## Intended uses & limitations Produces short summaries with the coherence of the XSUM Model #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Still has all the biases of any of the abstractive models, but seems a little less prone to hallucination. ## Training data Initialized with pegasus-XSUM ## Training procedure Trained for 11500 iterations on Gigaword corpus using OOB seq2seq (from hugging face using the default parameters) ## Eval results Evaluated on Gigaword test set (from hugging face using the default parameters) run_summarization.py --model_name_or_path pegasus-xsum/checkpoint-11500/ --do_predict --dataset_name gigaword --dataset_config "3.0.0" --source_prefix "summarize: " --output_dir pegasus-xsum --per_device_train_batch_size=8 --per_device_eval_batch_size=8 --overwrite_output_dir --predict_with_generate | Metric | Score | | ----------- | ----------- | | eval_rouge1 | 34.1958 | | eval_rouge2 | 15.4033 | | eval_rougeL | 31.4488 | run_summarization.py --model_name_or_path google/pegasus-gigaword --do_predict --dataset_name gigaword --dataset_config "3.0.0" --source_prefix "summarize: " --output_dir pegasus-xsum --per_device_train_batch_size=8 --per_device_eval_batch_size=8 --overwrite_output_dir --predict_with_generate | Metric | Score | | ----------- | ----------- | | eval_rouge1 | 20.8111 | | eval_rouge2 | 8.766 | | eval_rougeL | 18.4431 | ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```

Contrastive-Tension/BERT-Base-Swe-CT-STSb

feature-extraction

--- language: - en - es - eu datasets: - squad widget: - text: "When was Florence Nightingale born?" context: "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820." example_title: "English" - text: "¿Por qué provincias pasa el Tajo?" context: "El Tajo es el río más largo de la península ibérica, a la que atraviesa en su parte central, siguiendo un rumbo este-oeste, con una leve inclinación hacia el suroeste, que se acentúa cuando llega a Portugal, donde recibe el nombre de Tejo. Nace en los montes Universales, en la sierra de Albarracín, sobre la rama occidental del sistema Ibérico y, después de recorrer 1007 km, llega al océano Atlántico en la ciudad de Lisboa. En su desembocadura forma el estuario del mar de la Paja, en el que vierte un caudal medio de 456 m³/s. En sus primeros 816 km atraviesa España, donde discurre por cuatro comunidades autónomas (Aragón, Castilla-La Mancha, Madrid y Extremadura) y un total de seis provincias (Teruel, Guadalajara, Cuenca, Madrid, Toledo y Cáceres)." example_title: "Español" - text: "Zer beste izenak ditu Tartalo?" context: "Tartalo euskal mitologiako izaki begibakar artzain erraldoia da. Tartalo izena zenbait euskal hizkeratan herskari-bustidurarekin ahoskatu ohi denez, horrelaxe ere idazten da batzuetan: Ttarttalo. Euskal Herriko zenbait tokitan, Torto edo Anxo ere esaten diote." example_title: "Euskara" --- # ixambert-base-cased finetuned for QA This is a basic implementation of the multilingual model ["ixambert-base-cased"](https://huggingface.co/ixa-ehu/ixambert-base-cased), fine-tuned on SQuAD v1.1 and an experimental version of SQuAD1.1 in Basque (1/3 size of original SQuAD1.1), that is able to answer basic factual questions in English, Spanish and Basque. ## Overview * **Language model:** ixambert-base-cased * **Languages:** English, Spanish and Basque * **Downstream task:** Extractive QA * **Training data:** SQuAD v1.1 + experimental SQuAD1.1 in Basque * **Eval data:** SQuAD v1.1 + experimental SQuAD1.1 in Basque * **Infrastructure:** 1x GeForce RTX 2080 ## Outputs The model outputs the answer to the question, the start and end positions of the answer in the original context, and a score for the probability for that span of text to be the correct answer. For example: ```python {'score': 0.9667195081710815, 'start': 101, 'end': 105, 'answer': '1820'} ``` ## How to use ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "MarcBrun/ixambert-finetuned-squad-eu-en" # To get predictions context = "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820" question = "When was Florence Nightingale born?" qa = pipeline("question-answering", model=model_name, tokenizer=model_name) pred = qa(question=question,context=context) # To load the model and tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Hyperparameters ``` batch_size = 8 n_epochs = 3 learning_rate = 2e-5 optimizer = AdamW lr_schedule = linear max_seq_len = 384 doc_stride = 128 ```

Contrastive-Tension/BERT-Distil-CT-STSb

feature-extraction

--- language: - en - es - eu widget: - text: "When was Florence Nightingale born?" context: "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820." example_title: "English" - text: "¿Por qué provincias pasa el Tajo?" context: "El Tajo es el río más largo de la península ibérica, a la que atraviesa en su parte central, siguiendo un rumbo este-oeste, con una leve inclinación hacia el suroeste, que se acentúa cuando llega a Portugal, donde recibe el nombre de Tejo. Nace en los montes Universales, en la sierra de Albarracín, sobre la rama occidental del sistema Ibérico y, después de recorrer 1007 km, llega al océano Atlántico en la ciudad de Lisboa. En su desembocadura forma el estuario del mar de la Paja, en el que vierte un caudal medio de 456 m³/s. En sus primeros 816 km atraviesa España, donde discurre por cuatro comunidades autónomas (Aragón, Castilla-La Mancha, Madrid y Extremadura) y un total de seis provincias (Teruel, Guadalajara, Cuenca, Madrid, Toledo y Cáceres)." example_title: "Español" - text: "Zer beste izenak ditu Tartalo?" context: "Tartalo euskal mitologiako izaki begibakar artzain erraldoia da. Tartalo izena zenbait euskal hizkeratan herskari-bustidurarekin ahoskatu ohi denez, horrelaxe ere idazten da batzuetan: Ttarttalo. Euskal Herriko zenbait tokitan, Torto edo Anxo ere esaten diote." example_title: "Euskara" --- # ixambert-base-cased finetuned for QA This is a basic implementation of the multilingual model ["ixambert-base-cased"](https://huggingface.co/ixa-ehu/ixambert-base-cased), fine-tuned on an experimental version of SQuAD1.1 in Basque (1/3 size of original SQuAD1.1), that is able to answer basic factual questions. ## Overview * **Language model:** ixambert-base-cased * **Languages:** English, Spanish and Basque * **Downstream task:** Extractive QA * **Training data:** Experimental SQuAD1.1 in Basque * **Eval data:** Experimental SQuAD1.1 in Basque * **Infrastructure:** 1x GeForce RTX 2080 ## Outputs The model outputs the answer to the question, the start and end positions of the answer in the original context, and a score for the probability for that span of text to be the correct answer. For example: ```python {'score': 0.9667195081710815, 'start': 101, 'end': 105, 'answer': '1820'} ``` ## How to use ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "MarcBrun/ixambert-finetuned-squad-eu" # To get predictions context = "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820" question = "When was Florence Nightingale born?" qa = pipeline("question-answering", model=model_name, tokenizer=model_name) pred = qa(question=question,context=context) # To load the model and tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Hyperparameters ``` batch_size = 8 n_epochs = 3 learning_rate = 2e-5 optimizer = AdamW lr_schedule = linear max_seq_len = 384 doc_stride = 128 ```

Contrastive-Tension/BERT-Distil-CT

fill-mask

--- language: - en - es - eu datasets: - squad widget: - text: "When was Florence Nightingale born?" context: "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820." example_title: "English" - text: "¿Por qué provincias pasa el Tajo?" context: "El Tajo es el río más largo de la península ibérica, a la que atraviesa en su parte central, siguiendo un rumbo este-oeste, con una leve inclinación hacia el suroeste, que se acentúa cuando llega a Portugal, donde recibe el nombre de Tejo. Nace en los montes Universales, en la sierra de Albarracín, sobre la rama occidental del sistema Ibérico y, después de recorrer 1007 km, llega al océano Atlántico en la ciudad de Lisboa. En su desembocadura forma el estuario del mar de la Paja, en el que vierte un caudal medio de 456 m³/s. En sus primeros 816 km atraviesa España, donde discurre por cuatro comunidades autónomas (Aragón, Castilla-La Mancha, Madrid y Extremadura) y un total de seis provincias (Teruel, Guadalajara, Cuenca, Madrid, Toledo y Cáceres)." example_title: "Español" - text: "Zer beste izenak ditu Tartalo?" context: "Tartalo euskal mitologiako izaki begibakar artzain erraldoia da. Tartalo izena zenbait euskal hizkeratan herskari-bustidurarekin ahoskatu ohi denez, horrelaxe ere idazten da batzuetan: Ttarttalo. Euskal Herriko zenbait tokitan, Torto edo Anxo ere esaten diote." example_title: "Euskara" --- # ixambert-base-cased finetuned for QA This is a basic implementation of the multilingual model ["ixambert-base-cased"](https://huggingface.co/ixa-ehu/ixambert-base-cased), fine-tuned on SQuAD v1.1, that is able to answer basic factual questions in English, Spanish and Basque. ## Overview * **Language model:** ixambert-base-cased * **Languages:** English, Spanish and Basque * **Downstream task:** Extractive QA * **Training data:** SQuAD v1.1 * **Eval data:** SQuAD v1.1 * **Infrastructure:** 1x GeForce RTX 2080 ## Outputs The model outputs the answer to the question, the start and end positions of the answer in the original context, and a score for the probability for that span of text to be the correct answer. For example: ```python {'score': 0.9667195081710815, 'start': 101, 'end': 105, 'answer': '1820'} ``` ## How to use ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "MarcBrun/ixambert-finetuned-squad" # To get predictions context = "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820" question = "When was Florence Nightingale born?" qa = pipeline("question-answering", model=model_name, tokenizer=model_name) pred = qa(question=question,context=context) # To load the model and tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Hyperparameters ``` batch_size = 8 n_epochs = 3 learning_rate = 2e-5 optimizer = AdamW lr_schedule = linear max_seq_len = 384 doc_stride = 128 ```

Contrastive-Tension/RoBerta-Large-CT-STSb

feature-extraction

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-legal_data results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-legal_data This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.9101 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 26 | 5.3529 | | No log | 2.0 | 52 | 5.4226 | | No log | 3.0 | 78 | 5.2550 | | No log | 4.0 | 104 | 5.1011 | | No log | 5.0 | 130 | 5.1857 | | No log | 6.0 | 156 | 5.5119 | | No log | 7.0 | 182 | 5.4480 | | No log | 8.0 | 208 | 5.6993 | | No log | 9.0 | 234 | 5.9614 | | No log | 10.0 | 260 | 5.6987 | | No log | 11.0 | 286 | 5.6679 | | No log | 12.0 | 312 | 5.9850 | | No log | 13.0 | 338 | 5.6065 | | No log | 14.0 | 364 | 5.3162 | | No log | 15.0 | 390 | 5.7856 | | No log | 16.0 | 416 | 5.5786 | | No log | 17.0 | 442 | 5.6028 | | No log | 18.0 | 468 | 5.7649 | | No log | 19.0 | 494 | 5.5382 | | 1.8345 | 20.0 | 520 | 6.3654 | | 1.8345 | 21.0 | 546 | 5.3575 | | 1.8345 | 22.0 | 572 | 5.3808 | | 1.8345 | 23.0 | 598 | 5.9340 | | 1.8345 | 24.0 | 624 | 6.1475 | | 1.8345 | 25.0 | 650 | 6.2188 | | 1.8345 | 26.0 | 676 | 5.7651 | | 1.8345 | 27.0 | 702 | 6.2629 | | 1.8345 | 28.0 | 728 | 6.1356 | | 1.8345 | 29.0 | 754 | 5.9255 | | 1.8345 | 30.0 | 780 | 6.4252 | | 1.8345 | 31.0 | 806 | 5.6967 | | 1.8345 | 32.0 | 832 | 6.4324 | | 1.8345 | 33.0 | 858 | 6.5087 | | 1.8345 | 34.0 | 884 | 6.1113 | | 1.8345 | 35.0 | 910 | 6.7443 | | 1.8345 | 36.0 | 936 | 6.6970 | | 1.8345 | 37.0 | 962 | 6.5578 | | 1.8345 | 38.0 | 988 | 6.1963 | | 0.2251 | 39.0 | 1014 | 6.4893 | | 0.2251 | 40.0 | 1040 | 6.6347 | | 0.2251 | 41.0 | 1066 | 6.7106 | | 0.2251 | 42.0 | 1092 | 6.8129 | | 0.2251 | 43.0 | 1118 | 6.6386 | | 0.2251 | 44.0 | 1144 | 6.4134 | | 0.2251 | 45.0 | 1170 | 6.6883 | | 0.2251 | 46.0 | 1196 | 6.6406 | | 0.2251 | 47.0 | 1222 | 6.3065 | | 0.2251 | 48.0 | 1248 | 7.0281 | | 0.2251 | 49.0 | 1274 | 7.3646 | | 0.2251 | 50.0 | 1300 | 7.1086 | | 0.2251 | 51.0 | 1326 | 6.4749 | | 0.2251 | 52.0 | 1352 | 6.3303 | | 0.2251 | 53.0 | 1378 | 6.2919 | | 0.2251 | 54.0 | 1404 | 6.3855 | | 0.2251 | 55.0 | 1430 | 6.9501 | | 0.2251 | 56.0 | 1456 | 6.8714 | | 0.2251 | 57.0 | 1482 | 6.9856 | | 0.0891 | 58.0 | 1508 | 6.9910 | | 0.0891 | 59.0 | 1534 | 6.9293 | | 0.0891 | 60.0 | 1560 | 7.3493 | | 0.0891 | 61.0 | 1586 | 7.1834 | | 0.0891 | 62.0 | 1612 | 7.0479 | | 0.0891 | 63.0 | 1638 | 6.7674 | | 0.0891 | 64.0 | 1664 | 6.7553 | | 0.0891 | 65.0 | 1690 | 7.3074 | | 0.0891 | 66.0 | 1716 | 6.8071 | | 0.0891 | 67.0 | 1742 | 7.6622 | | 0.0891 | 68.0 | 1768 | 6.9555 | | 0.0891 | 69.0 | 1794 | 7.0153 | | 0.0891 | 70.0 | 1820 | 7.2085 | | 0.0891 | 71.0 | 1846 | 6.7582 | | 0.0891 | 72.0 | 1872 | 6.7989 | | 0.0891 | 73.0 | 1898 | 6.7012 | | 0.0891 | 74.0 | 1924 | 7.0088 | | 0.0891 | 75.0 | 1950 | 7.1024 | | 0.0891 | 76.0 | 1976 | 6.6968 | | 0.058 | 77.0 | 2002 | 7.5249 | | 0.058 | 78.0 | 2028 | 6.9199 | | 0.058 | 79.0 | 2054 | 7.1995 | | 0.058 | 80.0 | 2080 | 6.9349 | | 0.058 | 81.0 | 2106 | 7.4025 | | 0.058 | 82.0 | 2132 | 7.4199 | | 0.058 | 83.0 | 2158 | 6.8081 | | 0.058 | 84.0 | 2184 | 7.4777 | | 0.058 | 85.0 | 2210 | 7.1990 | | 0.058 | 86.0 | 2236 | 7.0062 | | 0.058 | 87.0 | 2262 | 7.5724 | | 0.058 | 88.0 | 2288 | 6.9362 | | 0.058 | 89.0 | 2314 | 7.1368 | | 0.058 | 90.0 | 2340 | 7.2183 | | 0.058 | 91.0 | 2366 | 6.8684 | | 0.058 | 92.0 | 2392 | 7.1433 | | 0.058 | 93.0 | 2418 | 7.2161 | | 0.058 | 94.0 | 2444 | 7.1442 | | 0.058 | 95.0 | 2470 | 7.3098 | | 0.058 | 96.0 | 2496 | 7.1264 | | 0.0512 | 97.0 | 2522 | 6.9424 | | 0.0512 | 98.0 | 2548 | 6.9155 | | 0.0512 | 99.0 | 2574 | 6.9038 | | 0.0512 | 100.0 | 2600 | 6.9101 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3

Cooker/cicero-similis

--- language: english datasets: - legal dataset pipeline_tag: question-answering ---

CouchCat/ma_sa_v7_distil

text-classification

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: bert-model-english results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # bert-model-english This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1408 - Train Sparse Categorical Accuracy: 0.9512 - Validation Loss: nan - Validation Sparse Categorical Accuracy: 0.0 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | 0.2775 | 0.8887 | nan | 0.0 | 0 | | 0.1702 | 0.9390 | nan | 0.0 | 1 | | 0.1300 | 0.9555 | nan | 0.0 | 2 | | 0.1346 | 0.9544 | nan | 0.0 | 3 | | 0.1408 | 0.9512 | nan | 0.0 | 4 | ### Framework versions - Transformers 4.16.2 - TensorFlow 2.7.0 - Datasets 1.18.3 - Tokenizers 0.11.0

CoveJH/ConBot

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: bert-model-english1 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # bert-model-english1 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0274 - Train Accuracy: 0.9914 - Validation Loss: 0.3493 - Validation Accuracy: 0.9303 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.0366 | 0.9885 | 0.3013 | 0.9299 | 0 | | 0.0261 | 0.9912 | 0.3445 | 0.9351 | 1 | | 0.0274 | 0.9914 | 0.3493 | 0.9303 | 2 | ### Framework versions - Transformers 4.16.2 - TensorFlow 2.7.0 - Datasets 1.18.3 - Tokenizers 0.11.0

Coyotl/DialoGPT-test-last-arthurmorgan

conversational

--- tags: - generated_from_keras_callback model-index: - name: beto_amazon_posneu results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # beto_amazon_posneu This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1277 - Train Accuracy: 0.9550 - Validation Loss: 0.3439 - Validation Accuracy: 0.8905 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.3195 | 0.8712 | 0.3454 | 0.8580 | 0 | | 0.1774 | 0.9358 | 0.3258 | 0.8802 | 1 | | 0.1277 | 0.9550 | 0.3439 | 0.8905 | 2 | ### Framework versions - Transformers 4.16.2 - TensorFlow 2.7.0 - Datasets 1.18.3 - Tokenizers 0.11.0

Coyotl/DialoGPT-test3-arthurmorgan

conversational

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: finetuned-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned-model This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8601 - Accuracy: 0.6117 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 84 | 0.8663 | 0.5914 | | No log | 2.0 | 168 | 0.8601 | 0.6117 | ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

Craak/GJ0001

--- license: mit tags: - generated_from_keras_callback model-index: - name: roberta-model-english results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-model-english This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1140 - Train Accuracy: 0.9596 - Validation Loss: 0.2166 - Validation Accuracy: 0.9301 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.2922 | 0.8804 | 0.2054 | 0.9162 | 0 | | 0.1710 | 0.9352 | 0.1879 | 0.9353 | 1 | | 0.1140 | 0.9596 | 0.2166 | 0.9301 | 2 | ### Framework versions - Transformers 4.16.2 - TensorFlow 2.7.0 - Tokenizers 0.11.0

CrisLeaf/generador-de-historias-de-tolkien

text-generation

# albertZero albertZero is a PyTorch model with a prediction head fine-tuned for SQuAD 2.0. Based on Hugging Face's albert-base-v2, albertZero employs a novel method to speed up fine-tuning. It re-initializes weights of final linear layer in the shared albert transformer block, resulting in a 2% point improvement during the early epochs of fine-tuning. ## Usage albertZero can be loaded like this: ```python tokenizer = AutoTokenizer.from_pretrained('MarshallHo/albertZero-squad2-base-v2') model = AutoModel.from_pretrained('MarshallHo/albertZero-squad2-base-v2') ``` or ```python from transformers import AlbertModel, AlbertTokenizer, AlbertForQuestionAnswering, AlbertPreTrainedModel mytokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') model = AlbertForQuestionAnsweringAVPool.from_pretrained('albert-base-v2') model.load_state_dict(torch.load('albertZero-squad2-base-v2.bin')) ``` ## References The goal of [ALBERT](https://arxiv.org/abs/1909.11942) is to reduce the memory requirement of the groundbreaking language model [BERT](https://arxiv.org/abs/1810.04805), while providing a similar level of performance. ALBERT mainly uses 2 methods to reduce the number of parameters – parameter sharing and factorized embedding. The field of NLP has undergone major improvements in recent years. The replacement of recurrent architectures by attention-based models has allowed NLP tasks such as question-answering to approach human level performance. In order to push the limits further, the [SQuAD2.0](https://arxiv.org/abs/1806.03822) dataset was created in 2018 with 50,000 additional unanswerable questions, addressing a major weakness of the original version of the dataset. At the time of writing, near the top of the [SQuAD2.0 leaderboard](https://rajpurkar.github.io/SQuAD-explorer/) is Shanghai Jiao Tong University’s [Retro-Reader](http://arxiv.org/abs/2001.09694). We have re-implemented their non-ensemble ALBERT model with the SQUAD2.0 prediction head. ## Acknowledgments Thanks to the generosity of the team at Hugging Face and all the groups referenced above !

Cthyllax/DialoGPT-medium-PaladinDanse

conversational

--- language: ru tags: - text-generation --- # GPT2 - RUS

Culmenus/checkpoint-168500-finetuned-de-to-is_nr2

--- language: ru tags: - text-generation license: apache-2.0 datasets: - bookcorpus - wikipedia --- # LSTM

Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2-finetuned-de-to-is_nr2

--- tags: - generated_from_trainer datasets: - opus_wikipedia model-index: - name: opus-mt-ar-en-finetuned-ar-to-en results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus_wikipedia type: opus_wikipedia args: ar-en --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-ar-en-finetuned-ar-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on the opus_wikipedia dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.10.0 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3

CurtisBowser/DialoGPT-small-sora

conversational

--- license: apache-2.0 tags: - generated_from_trainer datasets: - syssr_en_ar model-index: - name: opus-mt-en-ar-finetuned-Math-13-10-en-to-ar results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-ar-finetuned-Math-13-10-en-to-ar This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the syssr_en_ar dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.13.0 - Tokenizers 0.10.3

D3xter1922/distilbert-base-uncased-finetuned-cola

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: MaryaAI/opus-mt-en-ar-finetunedSTEM-v4-en-to-ar results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # MaryaAI/opus-mt-en-ar-finetunedSTEM-v4-en-to-ar This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0589 - Validation Loss: 5.3227 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.0589 | 5.3227 | 0 | ### Framework versions - Transformers 4.17.0.dev0 - TensorFlow 2.7.0 - Datasets 1.18.3.dev0 - Tokenizers 0.10.3

D3xter1922/electra-base-discriminator-finetuned-cola

text-classification

--- tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: opus-mt-en-ro-finetuned-en-to-ro results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 28.1599 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2886 - Bleu: 28.1599 - Gen Len: 34.1236 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.7437 | 1.0 | 38145 | 1.2886 | 28.1599 | 34.1236 | ### Framework versions - Transformers 4.10.0 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3

Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda

fill-mask

--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9187096774193548 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7703 - Accuracy: 0.9187 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2896 | 1.0 | 318 | 3.2887 | 0.7419 | | 2.6309 | 2.0 | 636 | 1.8797 | 0.8310 | | 1.5443 | 3.0 | 954 | 1.1537 | 0.8974 | | 1.0097 | 4.0 | 1272 | 0.8560 | 0.9135 | | 0.7918 | 5.0 | 1590 | 0.7703 | 0.9187 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

Davlan/bert-base-multilingual-cased-finetuned-naija

fill-mask

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1576 - F1: 0.8571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2924 | 1.0 | 715 | 0.1819 | 0.8286 | | 0.1503 | 2.0 | 1430 | 0.1580 | 0.8511 | | 0.0972 | 3.0 | 2145 | 0.1576 | 0.8571 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0

Davlan/bert-base-multilingual-cased-finetuned-swahili

fill-mask

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.862053266560437 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1354 - F1: 0.8621 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.254 | 1.0 | 525 | 0.1652 | 0.8254 | | 0.1293 | 2.0 | 1050 | 0.1431 | 0.8489 | | 0.0797 | 3.0 | 1575 | 0.1354 | 0.8621 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0

Davlan/bert-base-multilingual-cased-finetuned-wolof

fill-mask

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.en metrics: - name: F1 type: f1 value: 0.6807563959955506 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3856 - F1: 0.6808 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1038 | 1.0 | 50 | 0.5255 | 0.5331 | | 0.4922 | 2.0 | 100 | 0.4377 | 0.6379 | | 0.3664 | 3.0 | 150 | 0.3856 | 0.6808 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0

Davlan/bert-base-multilingual-cased-finetuned-yoruba

fill-mask

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8353494623655915 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2736 - F1: 0.8353 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5826 | 1.0 | 191 | 0.3442 | 0.7888 | | 0.2669 | 2.0 | 382 | 0.2848 | 0.8326 | | 0.1818 | 3.0 | 573 | 0.2736 | 0.8353 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0

Davlan/bert-base-multilingual-cased-masakhaner

token-classification

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8213114754098361 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2491 - F1: 0.8213 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8192 | 1.0 | 70 | 0.3300 | 0.7184 | | 0.2949 | 2.0 | 140 | 0.2817 | 0.7959 | | 0.189 | 3.0 | 210 | 0.2491 | 0.8213 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0

Davlan/mbart50-large-eng-yor-mt

text2text-generation

--- license: mit tags: - generated_from_trainer - de model-index: - name: feinesblack results: [] --- # feinschwarz This model is a fine-tuned version of [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2). The dataset was compiled from all texts of https://www.feinschwarz.net (as of October 2021). The homepage gathers essayistic texts on theological topics. The model will be used to explore the challenges of text-generating AI for theology with a hands on approach. Can an AI generate theological knowledge? Is a text by Karl Rahner of more value than an AI-generated text? Can we even distinguish a Rahner text from an AI-generated text in the future? And the crucial question: Would it be bad if not? The model is a very first attempt and in its current version certainly not yet a danger for academic theology 🤓 # Using the model You can create text with the model using this code: ```python from transformers import pipeline pipe = pipeline('text-generation', model="Michael711/feinschwarz", tokenizer="Michael711/feinschwarz") text = pipe("Der Sinn des Lebens ist es", max_length=100)[0]["generated_text"] print(text) ``` Have fun theologizing!

Davlan/mt5-small-en-pcm

text2text-generation

--- tags: - conversational --- # Harry Potter DialoGPT Model

Davlan/mt5_base_eng_yor_mt

text2text-generation

## About the model The model has been trained on a collection of 500k articles with headings. Its purpose is to create a one-line heading suitable for the given article. Sample code with a WikiNews article: ```python import torch from transformers import T5ForConditionalGeneration,T5Tokenizer device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = T5ForConditionalGeneration.from_pretrained("Michau/t5-base-en-generate-headline") tokenizer = T5Tokenizer.from_pretrained("Michau/t5-base-en-generate-headline") model = model.to(device) article = ''' Very early yesterday morning, the United States President Donald Trump reported he and his wife First Lady Melania Trump tested positive for COVID-19. Officials said the Trumps' 14-year-old son Barron tested negative as did First Family and Senior Advisors Jared Kushner and Ivanka Trump. Trump took to social media, posting at 12:54 am local time (0454 UTC) on Twitter, "Tonight, [Melania] and I tested positive for COVID-19. We will begin our quarantine and recovery process immediately. We will get through this TOGETHER!" Yesterday afternoon Marine One landed on the White House's South Lawn flying Trump to Walter Reed National Military Medical Center (WRNMMC) in Bethesda, Maryland. Reports said both were showing "mild symptoms". Senior administration officials were tested as people were informed of the positive test. Senior advisor Hope Hicks had tested positive on Thursday. Presidential physician Sean Conley issued a statement saying Trump has been given zinc, vitamin D, Pepcid and a daily Aspirin. Conley also gave a single dose of the experimental polyclonal antibodies drug from Regeneron Pharmaceuticals. According to official statements, Trump, now operating from the WRNMMC, is to continue performing his duties as president during a 14-day quarantine. In the event of Trump becoming incapacitated, Vice President Mike Pence could take over the duties of president via the 25th Amendment of the US Constitution. The Pence family all tested negative as of yesterday and there were no changes regarding Pence's campaign events. ''' text = "headline: " + article max_len = 256 encoding = tokenizer.encode_plus(text, return_tensors = "pt") input_ids = encoding["input_ids"].to(device) attention_masks = encoding["attention_mask"].to(device) beam_outputs = model.generate( input_ids = input_ids, attention_mask = attention_masks, max_length = 64, num_beams = 3, early_stopping = True, ) result = tokenizer.decode(beam_outputs[0]) print(result) ``` Result: ```Trump and First Lady Melania Test Positive for COVID-19```

Devmapall/paraphrase-quora

text2text-generation

--- tags: - conversational --- # Edward Elric DialoGPT Model

DiegoAlysson/opus-mt-en-ro-finetuned-en-to-ro

text2text-generation

--- language: - sk tags: - Slovak GPT-J - pytorch - causal-lm license: gpl-3.0 --- # Slovak GPT-J-1.4B Slovak GPT-J-1.4B with the whopping `1,415,283,792` parameters is the latest and the largest model released in Slovak GPT-J series. Smaller variants, [Slovak GPT-J-405M](https://huggingface.co/Milos/slovak-gpt-j-405M) and [Slovak GPT-J-162M](https://huggingface.co/Milos/slovak-gpt-j-162M), are still available. ## Model Description Model is based on [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax/) and has over 1.4B trainable parameters. <figure> | Hyperparameter | Value | |----------------------|----------------------------------------------------------------------------------------------------------------------------------------| | \\(n_{parameters}\\) | 1,415,283,792 | | \\(n_{layers}\\) | 24 | | \\(d_{model}\\) | 2048 | | \\(d_{ff}\\) | 16384 | | \\(n_{heads}\\) | 16 | | \\(d_{head}\\) | 256 | | \\(n_{ctx}\\) | 2048 | | \\(n_{vocab}\\) | 50256 (same tokenizer as GPT-2/3&dagger;) | | Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) | | RoPE Dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) | <p><strong>&dagger;</strong> ByteLevelBPETokenizer was trained on the same Slovak corpus.</p></figure> ## Training data Slovak GPT-J models were trained on a privately collected dataset consisting of predominantly Slovak text spanning different categories, e.g. web, news articles or even biblical texts - in total, over 40GB of text data was used to train this model. The dataset was preprocessed and cleaned in a specific way that involves minor but a few caveats, so in order to achieve the expected performance, feel free to refer to [How to use] section. Please, keep in mind that despite the effort to remove inappropriate corpus, the model still might generate sensitive content or leak sensitive information. ## Training procedure This model was trained for a bit more than 26.5 billion tokens over 48,001 steps on TPU v3-8 pod. The cross-entropy validation loss at the last step was `2.657`. ## Intended Use Same as the original GPT-J, Slovak GPT-J learns an inner representation of the language that can be used to extract features useful for downstream tasks, however, the intended use is text generation from a prompt. ### How to use This model along with the tokenizer can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Milos/slovak-gpt-j-1.4B") model = AutoModelForCausalLM.from_pretrained("Milos/slovak-gpt-j-1.4B") ``` When generating a prompt keep in mind these three things, and you should be good to go: 1. Never leave trailing whitespaces. There's a difference between how tokenizer encodes "Mám rád slovenčinu" (no space after `slovenčinu`) and "Mám rád slovenčinu " (trailing space after `slovenčinu`), i.e `[12805, 2872, 46878]` != `[12805, 2872, 46878, 221]`. 2. Always use good ol' US English primary double quotation marks, i.e. `""` instead of `„“`. 3. In case of a new line always enter `\n\n` instead of a single `\n` To illustrate an example of a basic text generation: ``` >>> prompt = "Tradičné jedlo na Orave sú" >>> encoded_input = tokenizer(prompt, return_tensors='pt') >>> output = model.generate(**encoded_input) >>> tokenizer.decode(output[0]) 'Tradičné jedlo na Orave sú bryndzové halušky\n\nNa Orave sa v minulosti varilo viac druhov' ``` ### Capabilities, Limitations, and Biases Based on the parameter count and our internal testing this should be by far the most capable model out of all models released in Slovak GPT-J series. For sake of simplicity, I have omitted all the boilerplate code and swapped `\n` for new lines in the examples that follow. These were generated with both `top_p` and `temperature` set to `0.9`. A spin on "Mor ho!" by Samo Chalpuka ``` PROMPT Zleteli orly z Tatry, tiahnu na podolia, ponad vysoké hory, ponad rovné polia; preleteli cez Dunaj, cez tú šíru vodu, sadli tam za pomedzím slovenského rodu. Duní Dunaj a luna za lunou sa valí OUTPUT Zleteli orly z Tatry, tiahnu na podolia, ponad vysoké hory, ponad rovné polia; preleteli cez Dunaj, cez tú šíru vodu, sadli tam za pomedzím slovenského rodu. Duní Dunaj a luna za lunou sa valí: nad ním svieti pevný hrad na vysokom bralí. Pod tým hradom je zem slovenská, na nej je Slovensko naše. Na tom vrchu leží slovenský národ, pod tým bralom leží krajina slovenská. A pod tým hradom leží i jeho sláva: na tom vrchu, na tom brale leží sláva slovenského národa.``` ``` Jokes are a bit hit or miss. Very often they contain vulgarisms and/or are highly politically incorrect. This one is rather exception than the rule. ``` PROMPT Nasledujú najlepšie vtipy. Prečo OUTPUT Nasledujú najlepšie vtipy. Prečo má policajt pri sebe vždy dva nože? Jeden je na sebaobranu, druhý na boj zblízka. ``` The infamous unicorn prompt from GPT-2 paper. ``` PROMPT V šokujúcom náleze vedci objavili stádo jednorožcov žijúcich v odľahlom, predtým nepreskúmanom údolí, v Andách. Ešte prekvapivejšie pre vedcov bol fakt, že jednorožce hovorili plynule po slovensky. OUTPUT V šokujúcom náleze vedci objavili stádo jednorožcov žijúcich v odľahlom, predtým nepreskúmanom údolí, v Andách. Ešte prekvapivejšie pre vedcov bol fakt, že jednorožce hovorili plynule po slovensky. V Andách ich totiž žilo približne 700 jedincov, ktoré sa živili výhradne materským mliekom. Slováci sú tak pravdepodobne prvými Európanmi, ktorí v Andách stretli jednorožca. "Je to dôkaz, že tieto zvieratá sú inteligentné a že žijú v Andách už stovky rokov," povedal pre webový portál televízie JOJ profesor geológie, geografie a zoológie, Milan Kováč. Podľa profesora Kováča si v Andách zvieratá vytvárajú svoj vlastný jazyk. Je to zároveň dôkaz, že jednorožce žili v minulosti aj v slovenských pohoriach. "Jednorožce sa tam síce vyskytovali, ale neboli tak dobre preskúmané, ako teraz v Andách." Na Slovensku však ľudia o jednorožcoch donedávna vedeli veľmi málo.<|endoftext|> ``` Since the dataset contains profanity, politically incorrect language, and (unintentionally) even a bits of text in Czech, the model can generate them in some extent too. Here's an example of the model output when prompt is in Czech: ``` >>> prompt = "Věta nesmí být sprostá a musí být zcela" >>> encoded_input = tokenizer(prompt, return_tensors='pt') >>> output = model.generate(**encoded_input, max_length=16) >>> tokenizer.decode(output[0]) 'Věta nesmí být sprostá a musí být zcela pravdivá.' ``` ## Citation and Related Information This was done as a moonlighting project during summer of 2021 to better understand transformers. I didn't have much free time to open source it properly, so it all sat on my hard drive until now :) If you use this model or have any questions about it feel free to hit me up at [twitter](https://twitter.com/miloskondela) or check out my [github](https://github.com/kondela) profile. ### BibTeX entry To cite this model: ```bibtex @misc{slovak-gpt-j-1.4B, author = {Kondela, Milos}, title = {{Slovak GPT-J-1.4B}}, howpublished = {\url{https://huggingface.co/Milos/slovak-gpt-j-1.4B}}, year = 2022, month = February } ``` To cite the codebase that trained this model: ```bibtex @misc{mesh-transformer-jax, author = {Wang, Ben}, title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}}, howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}}, year = 2021, month = May } ``` ## Acknowledgements This project was generously supported by [TPU Research Cloud (TRC) program](https://sites.research.google/trc/about/). Shoutout also goes to [Ben Wang](https://github.com/kingoflolz) and great [EleutherAI community](https://www.eleuther.ai/).

DiegoBalam12/institute_classification

--- language: - sk tags: - Slovak GPT-J - pytorch - causal-lm license: gpl-3.0 --- # Slovak GPT-J-162M Slovak GPT-J-162M is the first model released in Slovak GPT-J series and the very first publicly available transformer trained predominantly on Slovak corpus. Since the initial release two other models were made public, [Slovak GPT-J-405M](https://huggingface.co/Milos/slovak-gpt-j-405M) and the largest [Slovak GPT-J-1.4B](https://huggingface.co/Milos/slovak-gpt-j-1.4B). ## Model Description Model is based on [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax/) and has over 162M trainable parameters. <figure> | Hyperparameter | Value | |----------------------|-------------------------------------------------------------------------------------------------------------------------------| | \\(n_{parameters}\\) | 162,454,608 | | \\(n_{layers}\\) | 12 | | \\(d_{model}\\) | 768 | | \\(d_{ff}\\) | 16384 | | \\(n_{heads}\\) | 16 | | \\(d_{head}\\) | 256 | | \\(n_{ctx}\\) | 2048 | | \\(n_{vocab}\\) | 50256 (same tokenizer as GPT-2/3&dagger;) | | Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) | | RoPE Dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) | <p><strong>&dagger;</strong> ByteLevelBPETokenizer was trained on the same Slovak corpus.</p></figure> ## Training data Slovak GPT-J-162M was trained on a privately collected dataset consisting of predominantly Slovak text spanning different categories, e.g. web, news articles or even biblical texts - in total, over 40GB of text data was used to train this model. The dataset was preprocessed and cleaned in a specific way that involves minor but a few caveats, so in order to achieve the expected performance, feel free to refer to [How to use] section. Please, keep in mind that despite the effort to remove inappropriate parts of the corpus, the model still might generate sensitive content or leak sensitive information. ## Training procedure This model was trained for almost 37 billion tokens over 69,001 steps on TPU v3-8 pod. The cross-entropy validation loss at the last step was 3.065. ## Intended Use Same as the original GPT-J, Slovak GPT-J learns an inner representation of the language that can be used to extract features useful for downstream tasks, however, the intended use is text generation from a prompt. ### How to use This model along with the tokenizer can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Milos/slovak-gpt-j-162M") model = AutoModelForCausalLM.from_pretrained("Milos/slovak-gpt-j-162M") ``` When generating a prompt keep in mind these three things, and you should be good to go: 1. Never leave trailing whitespaces. There's a difference between how tokenizer encodes "Mám rád slovenčinu" (no space after `slovenčinu`) and "Mám rád slovenčinu " (trailing space after `slovenčinu`), i.e `[12805, 2872, 46878]` != `[12805, 2872, 46878, 221]`. 2. Always use good ol' US English primary double quotation marks, i.e. `""` instead of `„“`. 3. In case of a new line always enter `\n\n` instead of a single `\n` To illustrate an example of a basic text generation: ``` >>> prompt = "Moje najobľubenejšie mesto na severe Slovenska je" >>> encoded_input = tokenizer(prompt, return_tensors='pt') >>> output = model.generate(**encoded_input) >>> tokenizer.decode(output[0]) 'Moje najobľubenejšie mesto na severe Slovenska je Žilina.\n\nV Žiline sa nachádza množstvo zaujímavých miest' ``` ### Capabilities, Limitations, and Biases First and foremost, the capability of this particular model is very limited due to its relatively small size totalling only 162M parameters, hence the intended use of this particular model is to educate and have fun! :) Since the dataset contains profanity, politically incorrect language, and (unintentionally) even a bits of text in Czech, the model can generate them in some extent too. Here's an example of the model output when prompt is in Czech: ``` >>> prompt = "Věta nesmí být sprostá a musí být zcela" >>> encoded_input = tokenizer(prompt, return_tensors='pt') >>> output = model.generate(**encoded_input, max_length=16) >>> tokenizer.decode(output[0]) 'Věta nesmí být sprostá a musí být zcela věrná.' ``` ## Citation and Related Information This was done as a moonlighting project during summer of 2021 to better understand transformers. I didn't have much free time to open source it properly, so it all sat on my hard drive until now. Based on the popularity and interest in this model I might release _substantially_ larger versions of Slovak GPT-J models that are way more capable. If you use this model or have any questions about it feel free to hit me up at [twitter](https://twitter.com/miloskondela) or check out my [github](https://github.com/kondela) profile. ### BibTeX entry To cite this model: ```bibtex @misc{slovak-gpt-j-162m, author = {Kondela, Milos}, title = {{Slovak GPT-J-162M}}, howpublished = {\url{https://huggingface.co/Milos/slovak-gpt-j-162M}}, year = 2022, month = February } ``` To cite the codebase that trained this model: ```bibtex @misc{mesh-transformer-jax, author = {Wang, Ben}, title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}}, howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}}, year = 2021, month = May } ``` ## Acknowledgements This project was generously supported by [TPU Research Cloud (TRC) program](https://sites.research.google/trc/about/). Shoutout also goes to [Ben Wang](https://github.com/kingoflolz) and great [EleutherAI community](https://www.eleuther.ai/).

Digakive/Hsgshs

--- language: - sk tags: - Slovak GPT-J - pytorch - causal-lm license: gpl-3.0 --- # Slovak GPT-J-405M Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant [Slovak GPT-J-162M](https://huggingface.co/Milos/slovak-gpt-j-162M). Since then a larger [Slovak GPT-J-1.4B](https://huggingface.co/Milos/slovak-gpt-j-1.4B) was released. ## Model Description Model is based on [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax/) and has over 405M trainable parameters. <figure> | Hyperparameter | Value | |----------------------|----------------------------------------------------------------------------------------------------------------------------------------| | \\(n_{parameters}\\) | 405,677,136 | | \\(n_{layers}\\) | 24 | | \\(d_{model}\\) | 1024 | | \\(d_{ff}\\) | 16384 | | \\(n_{heads}\\) | 16 | | \\(d_{head}\\) | 256 | | \\(n_{ctx}\\) | 2048 | | \\(n_{vocab}\\) | 50256 (same tokenizer as GPT-2/3&dagger;) | | Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) | | RoPE Dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) | <p><strong>&dagger;</strong> ByteLevelBPETokenizer was trained on the same Slovak corpus.</p></figure> ## Training data Slovak GPT-J models were trained on a privately collected dataset consisting of predominantly Slovak text spanning different categories, e.g. web, news articles or even biblical texts - in total, over 40GB of text data was used to train this model. The dataset was preprocessed and cleaned in a specific way that involves minor but a few caveats, so in order to achieve the expected performance, feel free to refer to [How to use] section. Please, keep in mind that despite the effort to remove inappropriate corpus, the model still might generate sensitive content or leak sensitive information. ## Training procedure This model was trained for a bit more than 36.5 billion tokens over 69,001 steps on TPU v3-8 pod. The cross-entropy validation loss at the last step was `2.821`. ## Intended Use Same as the original GPT-J, Slovak GPT-J learns an inner representation of the language that can be used to extract features useful for downstream tasks, however, the intended use is text generation from a prompt. ### How to use This model along with the tokenizer can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Milos/slovak-gpt-j-405M") model = AutoModelForCausalLM.from_pretrained("Milos/slovak-gpt-j-405M") ``` When generating a prompt keep in mind these three things, and you should be good to go: 1. Never leave trailing whitespaces. There's a difference between how tokenizer encodes "Mám rád slovenčinu" (no space after `slovenčinu`) and "Mám rád slovenčinu " (trailing space after `slovenčinu`), i.e `[12805, 2872, 46878]` != `[12805, 2872, 46878, 221]`. 2. Always use good ol' US English primary double quotation marks, i.e. `""` instead of `„“`. 3. In case of a new line always enter `\n\n` instead of a single `\n` To illustrate an example of a basic text generation: ``` >>> prompt = "Tradičné jedlo na Orave sú" >>> encoded_input = tokenizer(prompt, return_tensors='pt') >>> output = model.generate(**encoded_input) >>> tokenizer.decode(output[0]) 'Tradičné jedlo na Orave sú bryndzové halušky\n\nNa Orave sa v minulosti varilo viac druhov' ``` ### Capabilities, Limitations, and Biases The capability of this particular model is somewhat decent despite its small size totalling 405M parameters. With relative ease it can manage to generate interesting and grammatically correct content. For example, you can try few of the following prompts. (For sake of simplicity, I have omitted all the boilerplate code and swapped `\n` for new lines). Try generating "How to" articles: ``` PROMPT Ako napísať pôsobivú esej krok za krokom OUTPUT Ako napísať pôsobivú esej krok za krokom V tomto článku sa dozviete, ako napísať esej, ktorá bude mať v sebe niečo, čo zaujme a bude sa vám páčiť. V prvom rade si musíte uvedomiť, že esej je vlastne písaný text, ktorý má byť napísaný na počítači.' ``` However, relying on the model to produce factually correct information isn't recommended. Or try to generate chat conversations: ``` PROMPT Peter: Ako sa mas? Lucia: Ale celkom dobre. Co si robil? Peter: OUTPUT Peter: Ako sa mas? Lucia: Ale celkom dobre. Co si robil? Peter: No, bol som na chate. Lucia: A co si tam robil? Peter: No, bol som tam s kamošmi. Lucia: A co si tam robil? Peter: No, bol som tam s kamošmi. ``` Apparently either Peters are more likely to act suspiciously in this particular context or there's a problem with the model. Let's assume that the second explanation may hold some merit. In general, GPT models can (and often will) get into a repeating cycle of generating same content. This is a common problem beyond the scope of this README, however, see [generate's documentation](https://huggingface.co/docs/transformers/master/en/main_classes/model#transformers.generation_utils.GenerationMixin.generate) on how to introduce a frequency/repetition penalty. Since the dataset contains profanity, politically incorrect language, and (unintentionally) even a bits of text in Czech, the model can generate them in some extent too. Here's an example of the model output when prompt is in Czech: ``` >>> prompt = "Věta nesmí být sprostá a musí být zcela" >>> encoded_input = tokenizer(prompt, return_tensors='pt') >>> output = model.generate(**encoded_input, max_length=16) >>> tokenizer.decode(output[0]) 'Věta nesmí být sprostá a musí být zcela pravdivá.' ``` ## Citation and Related Information This was done as a moonlighting project during summer of 2021 to better understand transformers. I didn't have much free time to open source it properly, so it all sat on my hard drive until now :) If you use this model or have any questions about it feel free to hit me up at [twitter](https://twitter.com/miloskondela) or check out my [github](https://github.com/kondela) profile. ### BibTeX entry To cite this model: ```bibtex @misc{slovak-gpt-j-405m, author = {Kondela, Milos}, title = {{Slovak GPT-J-405M}}, howpublished = {\url{https://huggingface.co/Milos/slovak-gpt-j-405M}}, year = 2022, month = February } ``` To cite the codebase that trained this model: ```bibtex @misc{mesh-transformer-jax, author = {Wang, Ben}, title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}}, howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}}, year = 2021, month = May } ``` ## Acknowledgements This project was generously supported by [TPU Research Cloud (TRC) program](https://sites.research.google/trc/about/). Shoutout also goes to [Ben Wang](https://github.com/kingoflolz) and great [EleutherAI community](https://www.eleuther.ai/).

Dilmk2/DialoGPT-small-harrypotter

conversational

--- language: - ru tags: - t5 license: - apache-2.0 inference: parameters: num_beams: 5 no_repeat_ngram_size: 4 widget: - text: "Что это за ерунда?" --- # RuT5Tox

Dkwkk/W

--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9239501818582607 - name: Recall type: recall value: 0.9378006488421524 - name: F1 type: f1 value: 0.9308238951809905 - name: Accuracy type: accuracy value: 0.9837800054013695 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0596 - Precision: 0.9240 - Recall: 0.9378 - F1: 0.9308 - Accuracy: 0.9838 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2381 | 1.0 | 878 | 0.0707 | 0.9100 | 0.9240 | 0.9170 | 0.9805 | | 0.0563 | 2.0 | 1756 | 0.0583 | 0.9246 | 0.9382 | 0.9314 | 0.9835 | | 0.03 | 3.0 | 2634 | 0.0596 | 0.9240 | 0.9378 | 0.9308 | 0.9838 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

DongHyoungLee/distilbert-base-uncased-finetuned-cola

text-classification

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: t5-small-finetuned-ro-to-en results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 13.4499 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-ro-to-en This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.5877 - Bleu: 13.4499 - Gen Len: 17.5073 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 1.6167 | 0.05 | 2000 | 1.8649 | 9.7029 | 17.5753 | | 1.4551 | 0.1 | 4000 | 1.7810 | 10.6382 | 17.5358 | | 1.3723 | 0.16 | 6000 | 1.7369 | 11.1285 | 17.5158 | | 1.3373 | 0.21 | 8000 | 1.7086 | 11.6173 | 17.5013 | | 1.2935 | 0.26 | 10000 | 1.6890 | 12.0641 | 17.5038 | | 1.2632 | 0.31 | 12000 | 1.6670 | 12.3012 | 17.5253 | | 1.2463 | 0.37 | 14000 | 1.6556 | 12.3991 | 17.5153 | | 1.2272 | 0.42 | 16000 | 1.6442 | 12.7392 | 17.4732 | | 1.2052 | 0.47 | 18000 | 1.6328 | 12.8446 | 17.5143 | | 1.1985 | 0.52 | 20000 | 1.6233 | 13.0892 | 17.4807 | | 1.1821 | 0.58 | 22000 | 1.6153 | 13.1529 | 17.4952 | | 1.1791 | 0.63 | 24000 | 1.6079 | 13.2964 | 17.5088 | | 1.1698 | 0.68 | 26000 | 1.6038 | 13.3548 | 17.4842 | | 1.154 | 0.73 | 28000 | 1.5957 | 13.3012 | 17.5053 | | 1.1634 | 0.79 | 30000 | 1.5931 | 13.4203 | 17.5083 | | 1.1487 | 0.84 | 32000 | 1.5893 | 13.3959 | 17.5123 | | 1.1495 | 0.89 | 34000 | 1.5875 | 13.3745 | 17.4902 | | 1.1458 | 0.94 | 36000 | 1.5877 | 13.4129 | 17.5043 | | 1.1465 | 1.0 | 38000 | 1.5877 | 13.4499 | 17.5073 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

Donghyun/L2_BERT

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: test-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test-finetuned This model is a fine-tuned version of [yhavinga/t5-v1.1-base-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cnn-test) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 1 | nan | 33.8462 | 31.746 | 30.7692 | 30.7692 | 86.0 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1 - Datasets 1.15.1 - Tokenizers 0.10.3