Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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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+---
+language:
+- ko
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:11668
+- loss:MultipleNegativesRankingLoss
+base_model: klue/bert-base
+widget:
+- source_sentence: klue-sts-v1_dev_00238
+  sentences:
+  - 이것은 7월 15일에 열린 주요 국가의 외무 장관들 간의 첫 번째 회담에 이은 것입니다.
+  - policy-rtt
+  - 이는 지난 15일 개최된 제1차 주요국 외교장관간 협의에 뒤이은 것이다.
+- source_sentence: klue-sts-v1_dev_00135
+  sentences:
+  - 3000만원 이하 소액대출은 지역신용보증재단 심사를 기업은행에 위탁하기로 했다.
+  - policy-rtt
+  - 3,000만원 미만의 소규모 대출은 기업은행에 의해 국내 신용보증재단을 검토하도록 의뢰될 것입니다.
+- source_sentence: klue-sts-v1_dev_00227
+  sentences:
+  - 그 공간은 4인 가족에게는 충분하지 않았습니다.
+  - 공간은 4명의 성인 가족이 사용하기에 부족함이 없었고.
+  - airbnb-rtt
+- source_sentence: klue-sts-v1_dev_00224
+  sentences:
+  - 타이페이 메인 역까지 걸어서 10분 정도 걸립니다.
+  - 클락키까지 걸어서 10분 정도 걸려요.
+  - airbnb-sampled
+- source_sentence: klue-sts-v1_dev_00159
+  sentences:
+  - 거실옆 작은 방에도 싱글 침대가 두개 있습니다.
+  - 2층에 얇은 벽 하나 사이로 방이 두 개 있습니다.
+  - airbnb-sampled
+datasets:
+- klue/klue
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on klue/bert-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [klue/bert-base](https://huggingface.co/klue/bert-base) on the [klue](https://huggingface.co/datasets/klue) 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:** [klue/bert-base](https://huggingface.co/klue/bert-base) <!-- at revision 77c8b3d707df785034b4e50f2da5d37be5f0f546 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [klue](https://huggingface.co/datasets/klue)
+- **Language:** ko
+<!-- - **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': 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("kgmyh/klue_bert-base_finetuning")
+# Run inference
+sentences = [
+    'klue-sts-v1_dev_00159',
+    'airbnb-sampled',
+    '2층에 얇은 벽 하나 사이로 방이 두 개 있습니다.',
+]
+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.*
+-->
+
+<!--
+## 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
+
+#### klue
+
+* Dataset: [klue](https://huggingface.co/datasets/klue) at [349481e](https://huggingface.co/datasets/klue/tree/349481ec73fff722f88e0453ca05c77a447d967c)
+* Size: 11,668 training samples
+* Columns: <code>guid</code>, <code>source</code>, <code>sentence1</code>, <code>sentence2</code>, and <code>labels</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | guid                                                                               | source                                                                            | sentence1                                                                         | sentence2                                                                         | labels             |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                            | string                                                                            | dict               |
+  | details | <ul><li>min: 17 tokens</li><li>mean: 17.91 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 10.01 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 19.55 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.35 tokens</li><li>max: 60 tokens</li></ul> | <ul><li></li></ul> |
+* Samples:
+  | guid                                 | source                       | sentence1                                                  | sentence2                                               | labels                                                                           |
+  |:-------------------------------------|:-----------------------------|:-----------------------------------------------------------|:--------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | <code>klue-sts-v1_train_00000</code> | <code>airbnb-rtt</code>      | <code>숙소 위치는 찾기 쉽고 일반적인 한국의 반지하 숙소입니다.</code>              | <code>숙박시설의 위치는 쉽게 찾을 수 있고 한국의 대표적인 반지하 숙박시설입니다.</code> | <code>{'label': 3.7, 'real-label': 3.714285714285714, 'binary-label': 1}</code>  |
+  | <code>klue-sts-v1_train_00001</code> | <code>policy-sampled</code>  | <code>위반행위 조사 등을 거부·방해·기피한 자는 500만원 이하 과태료 부과 대상이다.</code> | <code>시민들 스스로 자발적인 예방 노력을 한 것은 아산 뿐만이 아니었다.</code>      | <code>{'label': 0.0, 'real-label': 0.0, 'binary-label': 0}</code>                |
+  | <code>klue-sts-v1_train_00002</code> | <code>paraKQC-sampled</code> | <code>회사가 보낸 메일은 이 지메일이 아니라 다른 지메일 계정으로 전달해줘.</code>       | <code>사람들이 주로 네이버 메일을 쓰는 이유를 알려줘</code>                 | <code>{'label': 0.3, 'real-label': 0.3333333333333333, 'binary-label': 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"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### klue
+
+* Dataset: [klue](https://huggingface.co/datasets/klue) at [349481e](https://huggingface.co/datasets/klue/tree/349481ec73fff722f88e0453ca05c77a447d967c)
+* Size: 519 evaluation samples
+* Columns: <code>guid</code>, <code>source</code>, <code>sentence1</code>, <code>sentence2</code>, and <code>labels</code>
+* Approximate statistics based on the first 519 samples:
+  |         | guid                                                                               | source                                                                           | sentence1                                                                         | sentence2                                                                         | labels             |
+  |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------|
+  | type    | string                                                                             | string                                                                           | string                                                                            | string                                                                            | dict               |
+  | details | <ul><li>min: 17 tokens</li><li>mean: 17.82 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 9.72 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.47 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 19.42 tokens</li><li>max: 58 tokens</li></ul> | <ul><li></li></ul> |
+* Samples:
+  | guid                               | source                      | sentence1                                                                                     | sentence2                                                                                | labels                                                                          |
+  |:-----------------------------------|:----------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
+  | <code>klue-sts-v1_dev_00000</code> | <code>airbnb-rtt</code>     | <code>무엇보다도 호스트분들이 너무 친절하셨습니다.</code>                                                         | <code>무엇보다도, 호스트들은 매우 친절했습니다.</code>                                                     | <code>{'label': 4.9, 'real-label': 4.857142857142857, 'binary-label': 1}</code> |
+  | <code>klue-sts-v1_dev_00001</code> | <code>airbnb-sampled</code> | <code>주요 관광지 모두 걸어서 이동가능합니다.</code>                                                           | <code>위치는 피렌체 중심가까지 걸어서 이동 가능합니다.</code>                                                 | <code>{'label': 1.4, 'real-label': 1.428571428571429, 'binary-label': 0}</code> |
+  | <code>klue-sts-v1_dev_00002</code> | <code>policy-sampled</code> | <code>학생들의 균형 있는 영어능력을 향상시킬 수 있는 학교 수업을 유도하기 위해 2018학년도 수능부터 도입된 영어 영역 절대평가는 올해도 유지한다.</code> | <code>영어 영역의 경우 학생들이 한글 해석본을 암기하는 문제를 해소하기 위해 2016학년도부터 적용했던 EBS 연계 방식을 올해도 유지한다.</code> | <code>{'label': 1.3, 'real-label': 1.285714285714286, 'binary-label': 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"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 64
+- `weight_decay`: 0.01
+- `num_train_epochs`: 1
+- `warmup_steps`: 100
+- `load_best_model_at_end`: 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`: 16
+- `per_device_eval_batch_size`: 64
+- `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`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 100
+- `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`: False
+- `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`: True
+- `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`: 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
+- `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 |
+|:----------:|:-------:|:-------------:|:---------------:|
+| 0.0137     | 10      | 2.9128        | -               |
+| 0.0274     | 20      | 2.8336        | -               |
+| 0.0411     | 30      | 2.8053        | -               |
+| 0.0548     | 40      | 2.7919        | -               |
+| 0.0685     | 50      | 2.7815        | -               |
+| 0.0822     | 60      | 2.7722        | -               |
+| 0.0959     | 70      | 2.7779        | -               |
+| 0.1096     | 80      | 2.7768        | -               |
+| 0.1233     | 90      | 2.7846        | -               |
+| 0.1370     | 100     | 2.7747        | -               |
+| 0.1507     | 110     | 2.7786        | -               |
+| 0.1644     | 120     | 2.7719        | -               |
+| 0.1781     | 130     | 2.7745        | -               |
+| 0.1918     | 140     | 2.7747        | -               |
+| 0.2055     | 150     | 2.7749        | -               |
+| 0.2192     | 160     | 2.7715        | -               |
+| 0.2329     | 170     | 2.7863        | -               |
+| 0.2466     | 180     | 2.7732        | -               |
+| 0.2603     | 190     | 2.7744        | -               |
+| 0.2740     | 200     | 2.7754        | -               |
+| 0.2877     | 210     | 2.7726        | -               |
+| 0.3014     | 220     | 2.7718        | -               |
+| 0.3151     | 230     | 2.774         | -               |
+| 0.3288     | 240     | 2.7748        | -               |
+| 0.3425     | 250     | 2.7708        | -               |
+| 0.3562     | 260     | 2.7728        | -               |
+| 0.3699     | 270     | 2.7746        | -               |
+| 0.3836     | 280     | 2.7739        | -               |
+| 0.3973     | 290     | 2.7721        | -               |
+| 0.4110     | 300     | 2.7747        | -               |
+| 0.4247     | 310     | 2.7746        | -               |
+| 0.4384     | 320     | 2.7732        | -               |
+| 0.4521     | 330     | 2.7739        | -               |
+| 0.4658     | 340     | 2.7724        | -               |
+| 0.4795     | 350     | 2.7736        | -               |
+| 0.4932     | 360     | 2.7736        | -               |
+| 0.5068     | 370     | 2.7735        | -               |
+| 0.5205     | 380     | 2.7734        | -               |
+| 0.5342     | 390     | 2.7726        | -               |
+| 0.5479     | 400     | 2.7734        | -               |
+| 0.5616     | 410     | 2.7726        | -               |
+| 0.5753     | 420     | 2.7731        | -               |
+| 0.5890     | 430     | 2.7735        | -               |
+| 0.6027     | 440     | 2.7734        | -               |
+| 0.6164     | 450     | 2.7741        | -               |
+| 0.6301     | 460     | 2.7737        | -               |
+| 0.6438     | 470     | 2.7717        | -               |
+| 0.6575     | 480     | 2.7739        | -               |
+| 0.6712     | 490     | 2.7727        | -               |
+| **0.6849** | **500** | **2.7729**    | **4.129**       |
+| 0.6986     | 510     | 2.7723        | -               |
+| 0.7123     | 520     | 2.7729        | -               |
+| 0.7260     | 530     | 2.7736        | -               |
+| 0.7397     | 540     | 2.7725        | -               |
+| 0.7534     | 550     | 2.7735        | -               |
+| 0.7671     | 560     | 2.7737        | -               |
+| 0.7808     | 570     | 2.7731        | -               |
+| 0.7945     | 580     | 2.7733        | -               |
+| 0.8082     | 590     | 2.7725        | -               |
+| 0.8219     | 600     | 2.773         | -               |
+| 0.8356     | 610     | 2.7729        | -               |
+| 0.8493     | 620     | 2.7724        | -               |
+| 0.8630     | 630     | 2.7719        | -               |
+| 0.8767     | 640     | 2.7719        | -               |
+| 0.8904     | 650     | 2.7735        | -               |
+| 0.9041     | 660     | 2.7731        | -               |
+| 0.9178     | 670     | 2.7716        | -               |
+| 0.9315     | 680     | 2.7736        | -               |
+| 0.9452     | 690     | 2.7734        | -               |
+| 0.9589     | 700     | 2.7728        | -               |
+| 0.9726     | 710     | 2.7721        | -               |
+| 0.9863     | 720     | 2.7726        | -               |
+| 1.0        | 730     | 2.6339        | -               |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.12.7
+- Sentence Transformers: 3.3.1
+- Transformers: 4.48.0
+- PyTorch: 2.5.1+cpu
+- Accelerate: 1.1.1
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## 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",
+}
+```
+
+#### 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
+
+*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 +1,25 @@
+{
+  "_name_or_path": "klue/bert-base",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.48.0",
+    "pytorch": "2.5.1+cpu"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ba3639512bc254089899e0ffefef27b27ed7d53f43aa0af3be034b3b5933a27
+size 442491744

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "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

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

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}