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--- |
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language: es |
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license: mit |
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tags: |
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- generated_from_trainer |
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base_model: flax-community/spanish-t5-small |
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model-index: |
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- name: poem-gen-spanish-t5-small |
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results: [] |
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--- |
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# poem-gen-spanish-t5-small |
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This model is a fine-tuned version of [flax-community/spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on the [Spanish Poetry Dataset](https://www.kaggle.com/andreamorgar/spanish-poetry-dataset/version/1) dataset. |
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The model was created during the [First Spanish Hackathon](https://somosnlp.org/hackathon) organized by [Somos NLP](https://somosnlp.org/). |
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The team who participated was composed by: |
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- 🇨🇺 [Alberto Carmona Barthelemy](https://huggingface.co/milyiyo) |
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- 🇨🇴 [Jorge Henao](https://huggingface.co/jorge-henao) |
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- 🇪🇸 [Andrea Morales Garzón](https://huggingface.co/andreamorgar) |
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- 🇮🇳 [Drishti Sharma](https://huggingface.co/DrishtiSharma) |
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It achieves the following results on the evaluation set: |
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- Loss: 2.8707 |
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- Perplexity: 17.65 |
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## Model description |
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The model was trained to generate spanish poems attending to some parameters like style, sentiment, words to include and starting phrase. |
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Example: |
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``` |
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poema: |
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estilo: Pablo Neruda && |
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sentimiento: positivo && |
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palabras: cielo, luna, mar && |
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texto: Todos fueron a verle pasar |
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``` |
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### How to use |
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You can use this model directly with a pipeline for masked language modeling: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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model_name = 'hackathon-pln-es/poem-gen-spanish-t5-small' |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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author, sentiment, word, start_text = 'Pablo Neruda', 'positivo', 'cielo', 'Todos fueron a la plaza' |
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input_text = f"""poema: estilo: {author} && sentimiento: {sentiment} && palabras: {word} && texto: {start_text} """ |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(inputs["input_ids"], |
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do_sample = True, |
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max_length = 30, |
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repetition_penalty = 20.0, |
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top_k = 50, |
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top_p = 0.92) |
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detok_outputs = [tokenizer.decode(x, skip_special_tokens=True) for x in outputs] |
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res = detok_outputs[0] |
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``` |
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## Training and evaluation data |
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The original [dataset](https://www.kaggle.com/andreamorgar/spanish-poetry-dataset/version/1) has the columns `author`, `content` and `title`. |
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For each poem we generate new examples: |
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- content: *line_i* , generated: *line_i+1* |
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- content: *concatenate(line_i, line_i+1)* , generated: *line_i+2* |
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- content: *concatenate(line_i, line_i+1, line_i+2)* , generated: *line_i+3* |
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The resulting dataset has the columns `author`, `content`, `title` and `generated`. |
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For each example we compute the sentiment of the generated column and the nouns. In the case of sentiment, we used the model `mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis` and for nouns extraction we used spaCy. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 6 |
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- eval_batch_size: 6 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:------:|:---------------:| |
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| 2.7082 | 0.73 | 30000 | 2.8878 | |
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| 2.6251 | 1.46 | 60000 | 2.8940 | |
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| 2.5796 | 2.19 | 90000 | 2.8853 | |
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| 2.5556 | 2.93 | 120000 | 2.8749 | |
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| 2.527 | 3.66 | 150000 | 2.8850 | |
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| 2.5024 | 4.39 | 180000 | 2.8760 | |
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| 2.4887 | 5.12 | 210000 | 2.8749 | |
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| 2.4808 | 5.85 | 240000 | 2.8707 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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