Add new SentenceTransformer model

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "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:3560698
+- loss:ModifiedMatryoshkaLoss
+base_model: google-bert/bert-base-multilingual-cased
+widget:
+- source_sentence: This is a mine in Zimbabwe right now.
+  sentences:
+  - Esta es una mina de Zimbabwe en este momento.
+  - Transformar eso en una respuesta con forma matemática.
+  - Centrarse en el liderazgo, la diplomacia y el diseño institucional ayuda también
+    a explicar los intentos de paz que fracasan, o que no perduran.
+- source_sentence: '"You want me to deliver human rights throughout my global supply
+    chain.'
+  sentences:
+  - '"Quieres que respete los Derechos Humanos en la cadena mundial de suministro.'
+  - ¿Qué queremos decir cuando decimos que hacemos matemática... ...o que enseñamos
+    matemática?
+  - Así que criamos moscas cuyos cerebros fueron salpicados más o menos al azar con
+    células direccionables por la luz.
+- source_sentence: Figure out some of the other options that are much better.
+  sentences:
+  - En Kirguistán, en las últimas semanas, ocurrieron niveles de violencia sin precedentes
+    entre los kirguíes étnicos y los uzbecos étnicos.
+  - Piensen en otras de las opciones que son mucho mejores.
+  - La película sale -- la película es una versión en película de la presentación
+    de las diapositivas que di hace dos noches, excepto que es mucho más entretenida.
+- source_sentence: I've become very close with them, and they've welcomed me like
+    family.
+  sentences:
+  - he logrado una relación estrecha con ellos; soy como de la familia.
+  - O que los oídos se oigan a sí mismos... simplemente es imposible;
+  - Es un producto farmacéutico.
+- source_sentence: All the grayed-out species disappear.
+  sentences:
+  - 'Los diamantes: quizá todos hemos oído hablar de la película "Diamante de sangre".'
+  - Hay un vacío total de capital creativo en Bertie.
+  - Van a desaparecer todas las especies en gris.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- negative_mse
+model-index:
+- name: SentenceTransformer based on google-bert/bert-base-multilingual-cased
+  results:
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en es
+      type: MSE-val-en-es
+    metrics:
+    - type: negative_mse
+      value: -33.77506732940674
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en pt
+      type: MSE-val-en-pt
+    metrics:
+    - type: negative_mse
+      value: -34.092217683792114
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en pt br
+      type: MSE-val-en-pt-br
+    metrics:
+    - type: negative_mse
+      value: -32.07869827747345
+      name: Negative Mse
+---
+
+# SentenceTransformer based on google-bert/bert-base-multilingual-cased
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased). 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:** [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) <!-- at revision 3f076fdb1ab68d5b2880cb87a0886f315b8146f8 -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## 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("luanafelbarros/bert-en-es-pt-matryoshka_v3")
+# Run inference
+sentences = [
+    'All the grayed-out species disappear.',
+    'Van a desaparecer todas las especies en gris.',
+    'Los diamantes: quizá todos hemos oído hablar de la película "Diamante de sangre".',
+]
+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
+
+#### Knowledge Distillation
+
+* Datasets: `MSE-val-en-es`, `MSE-val-en-pt` and `MSE-val-en-pt-br`
+* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
+
+| Metric           | MSE-val-en-es | MSE-val-en-pt | MSE-val-en-pt-br |
+|:-----------------|:--------------|:--------------|:-----------------|
+| **negative_mse** | **-33.7751**  | **-34.0922**  | **-32.0787**     |
+
+<!--
+## 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
+
+#### Unnamed Dataset
+
+
+* Size: 3,560,698 training samples
+* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | english                                                                            | non_english                                                                        | label                                |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                             | string                                                                             | list                                 |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 25.46 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.67 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
+* Samples:
+  | english                                                                                                                                                | non_english                                                                                                                              | label                                                                                                                                 |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>And then there are certain conceptual things that can also benefit from hand calculating, but I think they're relatively small in number.</code> | <code>Y luego hay ciertas aspectos conceptuales que pueden beneficiarse del cálculo a mano pero creo que son relativamente pocos.</code> | <code>[-0.015244179405272007, 0.04601434990763664, -0.052873335778713226, 0.03535117208957672, -0.039562877267599106, ...]</code>     |
+  | <code>One thing I often ask about is ancient Greek and how this relates.</code>                                                                        | <code>Algo que pregunto a menudo es sobre el griego antiguo y cómo se relaciona.</code>                                                  | <code>[0.0012022971641272306, -0.009590390138328075, -0.032977133989334106, 0.017047710716724396, -0.0028919472824782133, ...]</code> |
+  | <code>See, the thing we're doing right now is we're forcing people to learn mathematics.</code>                                                        | <code>Vean, lo que estamos haciendo ahora es forzar a la gente a aprender matemáticas.</code>                                            | <code>[-0.01942082867026329, 0.1043599545955658, 0.009455358609557152, -0.02814248949289322, -0.017036128789186478, ...]</code>       |
+* Loss: <code>__main__.ModifiedMatryoshkaLoss</code> with these parameters:
+  ```json
+  {
+      "loss": "MSELoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 6,974 evaluation samples
+* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | english                                                                            | non_english                                                                        | label                                |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                             | string                                                                             | list                                 |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 25.68 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 27.31 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
+* Samples:
+  | english                                                                                                                                                    | non_english                                                                                                                                                       | label                                                                                                                              |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Thank you so much, Chris.</code>                                                                                                                     | <code>Muchas gracias Chris.</code>                                                                                                                                | <code>[-0.0616779662668705, -0.044504180550575256, -0.032505787909030914, -0.06641441583633423, 0.003981734160333872, ...]</code>  |
+  | <code>And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.</code>                                     | <code>Y es en verdad un gran honor tener la oportunidad de venir a este escenario por segunda vez. Estoy extremadamente agradecido.</code>                        | <code>[0.011398598551750183, -0.02500401996076107, -0.009884790517389774, 0.009336900897324085, 0.003082842566072941, ...]</code>  |
+  | <code>I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night.</code> | <code>He quedado conmovido por esta conferencia, y deseo agradecer a todos ustedes sus amables comentarios acerca de lo que tenía que decir la otra noche.</code> | <code>[-0.03842132166028023, 0.03635749593377113, -0.02491452544927597, -0.0032229204662144184, 0.0003549510147422552, ...]</code> |
+* Loss: <code>__main__.ModifiedMatryoshkaLoss</code> with these parameters:
+  ```json
+  {
+      "loss": "MSELoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 200
+- `per_device_eval_batch_size`: 200
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `label_names`: ['label']
+
+#### 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`: 200
+- `per_device_eval_batch_size`: 200
+- `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`: 2e-05
+- `weight_decay`: 0.0
+- `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`: 0.1
+- `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`: ['label']
+- `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
+- `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
+- `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
+- `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 | MSE-val-en-es_negative_mse | MSE-val-en-pt_negative_mse | MSE-val-en-pt-br_negative_mse |
+|:------:|:-----:|:-------------:|:---------------:|:--------------------------:|:--------------------------:|:-----------------------------:|
+| 0.0562 | 1000  | 0.0283        | 0.0251          | -22.4432                   | -22.0406                   | -25.1401                      |
+| 0.1123 | 2000  | 0.0241        | 0.0227          | -24.1255                   | -23.9880                   | -24.7731                      |
+| 0.1685 | 3000  | 0.0224        | 0.0214          | -25.3630                   | -25.2889                   | -25.4316                      |
+| 0.2247 | 4000  | 0.0214        | 0.0205          | -27.9225                   | -28.0038                   | -27.3050                      |
+| 0.2808 | 5000  | 0.0206        | 0.0199          | -29.4189                   | -29.5093                   | -28.8545                      |
+| 0.3370 | 6000  | 0.0202        | 0.0194          | -30.3190                   | -30.4212                   | -29.4919                      |
+| 0.3932 | 7000  | 0.0198        | 0.0191          | -31.3278                   | -31.4753                   | -30.3090                      |
+| 0.4493 | 8000  | 0.0195        | 0.0188          | -31.4089                   | -31.6387                   | -30.3325                      |
+| 0.5055 | 9000  | 0.0193        | 0.0186          | -32.0598                   | -32.2536                   | -30.9067                      |
+| 0.5617 | 10000 | 0.0191        | 0.0184          | -32.0989                   | -32.2766                   | -31.0155                      |
+| 0.6178 | 11000 | 0.0189        | 0.0183          | -32.2449                   | -32.4302                   | -30.9863                      |
+| 0.6740 | 12000 | 0.0187        | 0.0181          | -32.5800                   | -32.8070                   | -31.2254                      |
+| 0.7302 | 13000 | 0.0186        | 0.0180          | -32.9225                   | -33.1228                   | -31.5803                      |
+| 0.7863 | 14000 | 0.0185        | 0.0179          | -32.9227                   | -33.1304                   | -31.5169                      |
+| 0.8425 | 15000 | 0.0184        | 0.0178          | -33.0181                   | -33.2681                   | -31.5791                      |
+| 0.8987 | 16000 | 0.0183        | 0.0177          | -33.1309                   | -33.3638                   | -31.6113                      |
+| 0.9548 | 17000 | 0.0182        | 0.0176          | -33.1635                   | -33.4414                   | -31.6507                      |
+| 1.0110 | 18000 | 0.0181        | 0.0175          | -33.3615                   | -33.6376                   | -31.8086                      |
+| 1.0672 | 19000 | 0.018         | 0.0175          | -33.5781                   | -33.8775                   | -32.0611                      |
+| 1.1233 | 20000 | 0.0179        | 0.0174          | -33.5645                   | -33.8531                   | -32.0438                      |
+| 1.1795 | 21000 | 0.0179        | 0.0173          | -33.6646                   | -33.9817                   | -32.0500                      |
+| 1.2357 | 22000 | 0.0179        | 0.0173          | -33.7056                   | -34.0088                   | -32.1065                      |
+| 1.2918 | 23000 | 0.0178        | 0.0173          | -33.7397                   | -34.0153                   | -32.1810                      |
+| 1.3480 | 24000 | 0.0178        | 0.0172          | -33.7863                   | -34.0887                   | -32.1103                      |
+| 1.4042 | 25000 | 0.0177        | 0.0172          | -33.7981                   | -34.0863                   | -32.1683                      |
+| 1.4603 | 26000 | 0.0177        | 0.0171          | -33.7458                   | -34.0451                   | -32.0611                      |
+| 1.5165 | 27000 | 0.0177        | 0.0171          | -33.7650                   | -34.0652                   | -32.1565                      |
+| 1.5727 | 28000 | 0.0176        | 0.0171          | -33.7347                   | -34.0446                   | -32.0698                      |
+| 1.6288 | 29000 | 0.0176        | 0.0171          | -33.8011                   | -34.1169                   | -32.0683                      |
+| 1.6850 | 30000 | 0.0176        | 0.0170          | -33.7949                   | -34.1010                   | -32.1128                      |
+| 1.7412 | 31000 | 0.0176        | 0.0170          | -33.7713                   | -34.0857                   | -32.1020                      |
+| 1.7973 | 32000 | 0.0176        | 0.0170          | -33.8393                   | -34.1676                   | -32.1371                      |
+| 1.8535 | 33000 | 0.0175        | 0.0170          | -33.7687                   | -34.0887                   | -32.0748                      |
+| 1.9097 | 34000 | 0.0175        | 0.0170          | -33.7614                   | -34.0854                   | -32.0550                      |
+| 1.9659 | 35000 | 0.0175        | 0.0170          | -33.7751                   | -34.0922                   | -32.0787                      |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.3.1
+- Transformers: 4.46.3
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.1.1
+- Datasets: 3.1.0
+- Tokenizers: 0.20.3
+
+## 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 @@
+{
+  "_name_or_path": "google-bert/bert-base-multilingual-cased",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "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": "4.46.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 119547
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.46.3",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0bb3a2efcc34f1e9153c6bdc7faf9162c9c9f547ff61d4b37dbf7ab91d0f498c
+size 711436136

modules.json

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

sentence_bert_config.json

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+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

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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+      "special": true
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}