Upload folder using huggingface_hub

1_Pooling/config.json

@@ -3,5 +3,8 @@
   "pooling_mode_cls_token": true,
   "pooling_mode_mean_tokens": false,
   "pooling_mode_max_tokens": false,
-  "pooling_mode_mean_sqrt_len_tokens": false
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
 }

2_Dense/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:54ca435886648a25b6c7fbf1e52ca63915bddf61c7b7d61391c7f759a87418f7
+size 2362528

README.md

@@ -1,92 +1,368 @@
 ---
+base_model: sentence-transformers/LaBSE
+library_name: sentence-transformers
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
-- feature-extraction
 - sentence-similarity
-
+- feature-extraction
+- generated_from_trainer
+- dataset_size:101540
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Пилэн пытьыез ышиз.
+  sentences:
+  - — А знаете, ребята?
+  - Следы мальчика потеряны.
+  - — Ты прости меня, — иначе нельзя!
+- source_sentence: Огпол лушказ — пӧрмиз, нош дорын серекъязы, быгатэмез понна ушъязы,
+    со тӥни лушкаськонэз сямлы пӧрмытӥз.
+  sentences:
+  - Бабушка взяла хлеб и сунула одной корове.
+  - '- Сходи к Евгению Васильевичу, скажи - прошу его прийти!'
+  - Раз попробовал - ладно вышло, а дома посмеялись, похвалили за удачу, он и взял
+    воровство в обычай.
+- source_sentence: — Котькуд милиционер тонэн ӟечбуръяське.
+  sentences:
+  - — Что ни милиционер, так обязательно здоровается с тобой.
+  - — Ах, дорогой ПНШ, — сказал Егоров, кладя свою русую с седеющим хохолком голову
+    на оперативную сводку, — как хочется спать!
+  - Умею держать в руках и саблю острую.
+- source_sentence: Римской владычестволы пумит Испания но ӝутскиз табере.
+  sentences:
+  - Теперь против римского владычества поднялась Испания.
+  - Во время этих скитаний я сделал много полезных открытий.
+  - Потом они вместе с Алёнкой сели на бревно под солнышком сушиться.
+- source_sentence: Прошин со пыӵалэн туж умой ыбылӥз, сӧсырмем бераз кошкыкуз со пыӵалзэ
+    усто снайперлы — Жильцовлы сётыса кельтӥз.
+  sentences:
+  - Стрелял из нее Прошин отлично и, когда ушел в тыл после ранения, передал отличному
+    снайперу - Жильцову.
+  - – Чего стучишь? – сонным голосом спросила она.
+  - Валек по-прежнему лежал на траве и задумчиво следил за парившим в небе ястребом.
 ---
 
-# LaBSE finetuned on Udmurt-Russian parallel corpora (by [codemurt](https://huggingface.co/codemurt))
+# SentenceTransformer based on sentence-transformers/LaBSE
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
 
-<!--- Describe your model here -->
+## Model Details
 
-## Usage (Sentence-Transformers)
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision b7f947194ceae0ddf90bafe213722569e274ad28 -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
 
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
 
 ```
-pip install -U sentence-transformers
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+  (3): Normalize()
+)
 ```
 
-Then you can use the model like this:
+## Usage
+
+### Direct Usage (Sentence Transformers)
 
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Прошин со пыӵалэн туж умой ыбылӥз, сӧсырмем бераз кошкыкуз со пыӵалзэ усто снайперлы — Жильцовлы сётыса кельтӥз.',
+    'Стрелял из нее Прошин отлично и, когда ушел в тыл после ранения, передал отличному снайперу - Жильцову.',
+    '– Чего стучишь? – сонным голосом спросила она.',
+]
 embeddings = model.encode(sentences)
-print(embeddings)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
 ```
 
+<!--
+### Direct Usage (Transformers)
 
+<details><summary>Click to see the direct usage in Transformers</summary>
 
-## Evaluation Results
+</details>
+-->
 
-<!--- Describe how your model was evaluated -->
+<!--
+### Downstream Usage (Sentence Transformers)
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+You can finetune this model on your own dataset.
 
+<details><summary>Click to expand</summary>
 
-## Training
-The model was trained with the parameters:
+</details>
+-->
 
-**DataLoader**:
+<!--
+### Out-of-Scope Use
 
-`torch.utils.data.dataloader.DataLoader` of length 3615 with parameters:
-```
-{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
 
-**Loss**:
+<!--
+## Bias, Risks and Limitations
 
-`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
-  ```
-  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 101,540 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                        | label                                                         |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | float                                                         |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 31.78 tokens</li><li>max: 219 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 22.1 tokens</li><li>max: 147 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                              | sentence_1                                                       | label            |
+  |:--------------------------------------------------------|:-----------------------------------------------------------------|:-----------------|
+  | <code>Нырысь со чебер потэ но мылкыдэз шулдыртэ.</code> | <code>Сначала это кажется красивым и, возбуждая, веселит.</code> | <code>1.0</code> |
+  | <code>Тани султо но али ик кошко.</code>                | <code>Вот возьму и сейчас уеду.</code>                           | <code>1.0</code> |
+  | <code>— Мынӥсько! — вазиз анай.</code>                  | <code>— Иду! — ответила мать.</code>                             | <code>1.0</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
   ```
 
-Parameters of the fit()-Method:
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 10
+- `per_device_eval_batch_size`: 10
+- `num_train_epochs`: 1
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 10
+- `per_device_eval_batch_size`: 10
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `eval_use_gather_object`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0787 | 100  | -             |
+| 0.1575 | 200  | -             |
+| 0.2362 | 300  | -             |
+| 0.3150 | 400  | -             |
+| 0.3937 | 500  | 0.3765        |
+| 0.4724 | 600  | -             |
+| 0.5512 | 700  | -             |
+| 0.6299 | 800  | -             |
+
+
+### Framework Versions
+- Python: 3.9.18
+- Sentence Transformers: 3.4.0
+- Transformers: 4.44.0
+- PyTorch: 2.4.0+cu121
+- Accelerate: 0.33.0
+- Datasets: 3.2.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
 ```
-{
-    "epochs": 1,
-    "evaluation_steps": 100,
-    "evaluator": "__main__.ChainScoreEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "warmupcosine",
-    "steps_per_epoch": null,
-    "warmup_steps": 200,
-    "weight_decay": 0.01
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
 }
 ```
 
+<!--
+## Glossary
 
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
-  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
-  (3): Normalize()
-)
-```
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
 
-## Citing & Authors
+<!--
+## Model Card Contact
 
-<!--- Describe where people can find more information -->
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "/home/jovyan/.cache/torch/sentence_transformers/sentence-transformers_LaBSE/",
+  "_name_or_path": "sentence-transformers/LaBSE",
   "architectures": [
     "BertModel"
   ],
@@ -25,7 +25,7 @@
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
   "torch_dtype": "float32",
-  "transformers_version": "4.37.1",
+  "transformers_version": "4.44.0",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 501153

config_sentence_transformers.json

@@ -1,7 +1,10 @@
 {
   "__version__": {
-    "sentence_transformers": "2.0.0",
-    "transformers": "4.7.0",
-    "pytorch": "1.9.0+cu102"
-  }
+    "sentence_transformers": "3.4.0",
+    "transformers": "4.44.0",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4a48ac248c99b8025ad005116e4c97a246ccb4524a82b6669a777535848522d5
+oid sha256:0a02134ed5c43d19f204ed7b24ec9b7d3b2d88c9d4ce4732907af51d61248154
 size 1883730160

tokenizer_config.json

@@ -47,7 +47,7 @@
   "do_lower_case": false,
   "full_tokenizer_file": null,
   "mask_token": "[MASK]",
-  "model_max_length": 512,
+  "model_max_length": 256,
   "never_split": null,
   "pad_token": "[PAD]",
   "sep_token": "[SEP]",