Add new SentenceTransformer model

1_Pooling/config.json

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+{
+  "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
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:111476
+- loss:CosineSimilarityLoss
+base_model: sergeyzh/LaBSE-ru-sts
+widget:
+- source_sentence: 'трюковый самокат plank 180 белый '
+  sentences:
+  - смарт-телевизор 75 sony kd-75x950h
+  - самокат для трюков плэнк 1.80 м белый
+  - xiaomi mi 11 8gb 128gb
+- source_sentence: 'вейп vaporesso xros '
+  sentences:
+  - садовая ограда классика 4 2 м белый
+  - кухонные весы
+  - электронная сигарета voopoo drag
+- source_sentence: серьги l atelier precieux 1628710
+  sentences:
+  - фильтр hepa для пылесоса варис st400
+  - потолочная люстра майтон nostalgia ceiling chandelier mod048pl-06g
+  - серьги atelier de bijoux 1628712
+- source_sentence: 'мобильный геймпад триггерами x2 '
+  sentences:
+  - электроскутер nitro pro milano 750w led
+  - наушники без проводов мейзу ep52 lite
+  - геймпад с функцией триггеров x2
+- source_sentence: комод 7 рисунком машинки 4 ящика
+  sentences:
+  - удлинитель far f 505 d lara выключателем 2 0м
+  - беззеркальный фотоаппарат nikon z50 kit 16-50mm ilce-7cl красный
+  - комод 8 с изображением супергероев 6 ящиков
+datasets:
+- seregadgl/data_cross_gpt_139k
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+- cosine_accuracy_threshold
+- cosine_f1
+- cosine_f1_threshold
+- cosine_precision
+- cosine_recall
+- cosine_ap
+- cosine_mcc
+model-index:
+- name: SentenceTransformer based on sergeyzh/LaBSE-ru-sts
+  results:
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: eval
+      type: eval
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9722640832436311
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.630459189414978
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.9724366041896361
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.5821653008460999
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.9647847565278758
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.9802107980210798
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.9945729266353226
+      name: Cosine Ap
+    - type: cosine_mcc
+      value: 0.9445047865635516
+      name: Cosine Mcc
+---
+
+# SentenceTransformer based on sergeyzh/LaBSE-ru-sts
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sergeyzh/LaBSE-ru-sts](https://huggingface.co/sergeyzh/LaBSE-ru-sts) on the [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) dataset. 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sergeyzh/LaBSE-ru-sts](https://huggingface.co/sergeyzh/LaBSE-ru-sts) <!-- at revision 00c333ce29c9ad739f48baca9a578cd1e85094d4 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, '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): Normalize()
+)
+```
+
+## 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
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("seregadgl/sts_v11")
+# Run inference
+sentences = [
+    'комод 7 рисунком машинки 4 ящика',
+    'комод 8 с изображением супергероев 6 ящиков',
+    'беззеркальный фотоаппарат nikon z50 kit 16-50mm ilce-7cl красный',
+]
+embeddings = model.encode(sentences)
+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>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Binary Classification
+
+* Dataset: `eval`
+* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
+
+| Metric                    | Value      |
+|:--------------------------|:-----------|
+| cosine_accuracy           | 0.9723     |
+| cosine_accuracy_threshold | 0.6305     |
+| cosine_f1                 | 0.9724     |
+| cosine_f1_threshold       | 0.5822     |
+| cosine_precision          | 0.9648     |
+| cosine_recall             | 0.9802     |
+| **cosine_ap**             | **0.9946** |
+| cosine_mcc                | 0.9445     |
+
+<!--
+## Bias, Risks and Limitations
+
+*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
+
+#### data_cross_gpt_139k
+
+* Dataset: [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) at [9e1f5ca](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k/tree/9e1f5ca30088e6f61ca5b9a742b38ef2c4fc7f3e)
+* Size: 111,476 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                         | label                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 14.84 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.64 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                              | sentence2                                                                                           | label            |
+  |:-------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>нож кухонный 21см синий</code>                                                                   | <code>кухонный нож 22см зелёный</code>                                                              | <code>0.0</code> |
+  | <code>блок питания универсальный для мерцающих флэш гирлянд rich led бахрома занавес нить белый</code> | <code>адаптер питания для мигающих led гирлянд "luminous decor" бахрома занавес нить зелёный</code> | <code>0.0</code> |
+  | <code>защитная пленка для apple iphone 6 прозрачная </code>                                            | <code>protective film for apple iphone 6 transparent</code>                                         | <code>1.0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### data_cross_gpt_139k
+
+* Dataset: [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) at [9e1f5ca](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k/tree/9e1f5ca30088e6f61ca5b9a742b38ef2c4fc7f3e)
+* Size: 27,870 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                         | label                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 15.05 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.57 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                        | sentence2                                                               | label            |
+  |:---------------------------------------------------------------------------------|:------------------------------------------------------------------------|:-----------------|
+  | <code>сумка дорожная складная полет оранжевая bradex td 0599 </code>             | <code>сумка для путешествий складная брадекс orange</code>              | <code>1.0</code> |
+  | <code>наушники sennheiser hd 450bt белый </code>                                 | <code>наушники сенхайзер hd 450bt white</code>                          | <code>1.0</code> |
+  | <code>перчатки stg al-05-1871 синие серые черные зеленыеполноразмерные xl</code> | <code>перчатки stg al-05-1871 blue gray black green full size xl</code> | <code>1.0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `learning_rate`: 4.7459131195420915e-05
+- `weight_decay`: 0.03196240090522689
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.014344463935915175
+- `fp16`: True
+
+#### 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`: 32
+- `per_device_eval_batch_size`: 32
+- `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`: 4.7459131195420915e-05
+- `weight_decay`: 0.03196240090522689
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.014344463935915175
+- `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}
+- `tp_size`: 0
+- `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`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `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
+- `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
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | eval_cosine_ap |
+|:------:|:----:|:-------------:|:---------------:|:--------------:|
+| 0.0287 | 100  | 0.189         | -               | -              |
+| 0.0574 | 200  | 0.0695        | -               | -              |
+| 0.0861 | 300  | 0.067         | -               | -              |
+| 0.1148 | 400  | 0.0643        | -               | -              |
+| 0.1435 | 500  | 0.0594        | 0.0549          | 0.9862         |
+| 0.1722 | 600  | 0.0565        | -               | -              |
+| 0.2009 | 700  | 0.0535        | -               | -              |
+| 0.2296 | 800  | 0.0506        | -               | -              |
+| 0.2583 | 900  | 0.0549        | -               | -              |
+| 0.2870 | 1000 | 0.0535        | 0.0451          | 0.9888         |
+| 0.3157 | 1100 | 0.0492        | -               | -              |
+| 0.3444 | 1200 | 0.0499        | -               | -              |
+| 0.3731 | 1300 | 0.0486        | -               | -              |
+| 0.4018 | 1400 | 0.0458        | -               | -              |
+| 0.4305 | 1500 | 0.0458        | 0.0419          | 0.9877         |
+| 0.4592 | 1600 | 0.0502        | -               | -              |
+| 0.4879 | 1700 | 0.045         | -               | -              |
+| 0.5166 | 1800 | 0.0435        | -               | -              |
+| 0.5454 | 1900 | 0.0426        | -               | -              |
+| 0.5741 | 2000 | 0.0422        | 0.0386          | 0.9906         |
+| 0.6028 | 2100 | 0.0436        | -               | -              |
+| 0.6315 | 2200 | 0.043         | -               | -              |
+| 0.6602 | 2300 | 0.0432        | -               | -              |
+| 0.6889 | 2400 | 0.0397        | -               | -              |
+| 0.7176 | 2500 | 0.0394        | 0.0357          | 0.9903         |
+| 0.7463 | 2600 | 0.039         | -               | -              |
+| 0.7750 | 2700 | 0.0398        | -               | -              |
+| 0.8037 | 2800 | 0.0394        | -               | -              |
+| 0.8324 | 2900 | 0.0426        | -               | -              |
+| 0.8611 | 3000 | 0.0345        | 0.0341          | 0.9921         |
+| 0.8898 | 3100 | 0.0361        | -               | -              |
+| 0.9185 | 3200 | 0.0365        | -               | -              |
+| 0.9472 | 3300 | 0.0401        | -               | -              |
+| 0.9759 | 3400 | 0.0391        | -               | -              |
+| 1.0046 | 3500 | 0.0342        | 0.0310          | 0.9928         |
+| 1.0333 | 3600 | 0.0267        | -               | -              |
+| 1.0620 | 3700 | 0.0264        | -               | -              |
+| 1.0907 | 3800 | 0.0263        | -               | -              |
+| 1.1194 | 3900 | 0.0248        | -               | -              |
+| 1.1481 | 4000 | 0.0282        | 0.0301          | 0.9928         |
+| 1.1768 | 4100 | 0.0279        | -               | -              |
+| 1.2055 | 4200 | 0.0258        | -               | -              |
+| 1.2342 | 4300 | 0.0248        | -               | -              |
+| 1.2629 | 4400 | 0.0289        | -               | -              |
+| 1.2916 | 4500 | 0.0261        | 0.0291          | 0.9935         |
+| 1.3203 | 4600 | 0.0262        | -               | -              |
+| 1.3490 | 4700 | 0.0276        | -               | -              |
+| 1.3777 | 4800 | 0.0256        | -               | -              |
+| 1.4064 | 4900 | 0.0272        | -               | -              |
+| 1.4351 | 5000 | 0.0283        | 0.0284          | 0.9939         |
+| 1.4638 | 5100 | 0.0254        | -               | -              |
+| 1.4925 | 5200 | 0.0252        | -               | -              |
+| 1.5212 | 5300 | 0.0234        | -               | -              |
+| 1.5499 | 5400 | 0.0228        | -               | -              |
+| 1.5786 | 5500 | 0.0248        | 0.0277          | 0.9941         |
+| 1.6073 | 5600 | 0.024         | -               | -              |
+| 1.6361 | 5700 | 0.0225        | -               | -              |
+| 1.6648 | 5800 | 0.0234        | -               | -              |
+| 1.6935 | 5900 | 0.0226        | -               | -              |
+| 1.7222 | 6000 | 0.0248        | 0.0265          | 0.9942         |
+| 1.7509 | 6100 | 0.0247        | -               | -              |
+| 1.7796 | 6200 | 0.0219        | -               | -              |
+| 1.8083 | 6300 | 0.026         | -               | -              |
+| 1.8370 | 6400 | 0.0209        | -               | -              |
+| 1.8657 | 6500 | 0.0252        | 0.0262          | 0.9945         |
+| 1.8944 | 6600 | 0.0218        | -               | -              |
+| 1.9231 | 6700 | 0.0223        | -               | -              |
+| 1.9518 | 6800 | 0.0228        | -               | -              |
+| 1.9805 | 6900 | 0.0242        | -               | -              |
+| 2.0    | 6968 | -             | 0.0257          | 0.9946         |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.2
+- Datasets: 3.6.0
+- Tokenizers: 0.21.1
+
+## 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",
+}
+```
+
+<!--
+## Glossary
+
+*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.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,31 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 55083
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2637b47b58bbabb731a78ac67800dc83f3b31d681fd10490d8e3ae3d08ad7d91
+size 513402728

modules.json

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+[
+  {
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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
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+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "single_word": false
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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}