Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "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
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:11668
+- loss:CosineSimilarityLoss
+datasets:
+- klue/klue
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+widget:
+- source_sentence: 이는 지난 15일 개최된 제1차 주요국 외교장관간 협의에 뒤이은 것이다.
+  sentences:
+  - 100일간의 유럽 여행 중 단연 최고의 숙소였습니다!
+  - 이것은 7월 15일에 열린 주요 국가의 외무 장관들 간의 첫 번째 회담에 이은 것입니다.
+  - 거실옆 작은 방에도 싱글 침대가 두개 있습니다.
+- source_sentence: 3000만원 이하 소액대출은 지역신용보증재단 심사를 기업은행에 위탁하기로 했다.
+  sentences:
+  - 그 집은 두 사람이 살기에 충분히 크고 깨끗했습니다.
+  - 3,000만원 미만의 소규모 대출은 기업은행에 의해 국내 신용보증재단을 검토하도록 의뢰될 것입니다.
+  - 지하철, 버스, 기차 모두 편리했습니다.
+- source_sentence: 공간은 4명의 성인 가족이 사용하기에 부족함이 없었고.
+  sentences:
+  - 특히 모든 부처 장관들이 책상이 아닌 현장에서 직접 방역과 민생 경제의 중심에 서 주시기 바랍니다.
+  - 구시가까지 걸어서 15분 정도 걸립니다.
+  - 그 공간은 4인 가족에게는 충분하지 않았습니다.
+- source_sentence: 클락키까지 걸어서 10분 정도 걸려요.
+  sentences:
+  - 가족 여행이나 4명정도 같이 가는 일행은 정말 좋은 곳 같아요
+  - 외출 시 방범 모드는 어떻게 바꿔?
+  - 타이페이 메인 역까지 걸어서 10분 정도 걸립니다.
+- source_sentence: SR은 동대구·김천구미·신경주역에서 승하차하는 모든 국민에게 운임 10%를 할인해 준다.
+  sentences:
+  - 그 방은 두 사람이 쓰기에는 조금 좁아요.
+  - 수강신청 하는 날짜가 어느 날짜인지 아시는지요?
+  - SR은 동대구역, 김천구미역, 신주역을 오가는 모든 승객을 대상으로 요금을 10% 할인해 드립니다.
+pipeline_tag: sentence-similarity
+model-index:
+- name: SentenceTransformer
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev
+      type: sts-dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.8423082829850602
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8421532006158673
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8160367390495141
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8190120467928964
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8155293657289817
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8181098597355426
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.7792779317567364
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.7861496535827294
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8423082829850602
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8421532006158673
+      name: Spearman Max
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained on the [klue/klue](https://huggingface.co/datasets/klue/klue) dataset. It maps sentences & paragraphs to a 1024-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:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [klue/klue](https://huggingface.co/datasets/klue/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: RobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, '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("snunlp/KLUE-SRoBERTa-Large-SNUExtended-klueNLI-klueSTS")
+# Run inference
+sentences = [
+    'SR은 동대구·김천구미·신경주역에서 승하차하는 모든 국민에게 운임 10%를 할인해 준다.',
+    'SR은 동대구역, 김천구미역, 신주역을 오가는 모든 승객을 대상으로 요금을 10% 할인해 드립니다.',
+    '수강신청 하는 날짜가 어느 날짜인지 아시는지요?',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# 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
+
+#### Semantic Similarity
+* Dataset: `sts-dev`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8423     |
+| **spearman_cosine** | **0.8422** |
+| pearson_manhattan   | 0.816      |
+| spearman_manhattan  | 0.819      |
+| pearson_euclidean   | 0.8155     |
+| spearman_euclidean  | 0.8181     |
+| pearson_dot         | 0.7793     |
+| spearman_dot        | 0.7861     |
+| pearson_max         | 0.8423     |
+| spearman_max        | 0.8422     |
+
+<!--
+## 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/klue
+
+* Dataset: [klue/klue](https://huggingface.co/datasets/klue/klue) at [349481e](https://huggingface.co/datasets/klue/klue/tree/349481ec73fff722f88e0453ca05c77a447d967c)
+* Size: 11,668 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: 8 tokens</li><li>mean: 22.48 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 21.92 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                  | sentence2                                               | label                            |
+  |:-----------------------------------------------------------|:--------------------------------------------------------|:---------------------------------|
+  | <code>숙소 위치는 찾기 쉽고 일반적인 한국의 반지하 숙소입니다.</code>              | <code>숙박시설의 위치는 쉽게 찾을 수 있고 한국의 대표적인 반지하 숙박시설입니다.</code> | <code>0.7428571428571428</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>0.0</code>                 |
+  | <code>회사가 보낸 메일은 이 지메일이 아니라 다른 지메일 계정으로 전달해줘.</code>       | <code>사람들이 주로 네이버 메일을 쓰는 이유를 알려줘</code>                 | <code>0.06666666666666667</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
+
+#### klue/klue
+
+* Dataset: [klue/klue](https://huggingface.co/datasets/klue/klue) at [349481e](https://huggingface.co/datasets/klue/klue/tree/349481ec73fff722f88e0453ca05c77a447d967c)
+* Size: 519 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: 8 tokens</li><li>mean: 22.45 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 21.86 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                     | sentence2                                                                                | label                            |
+  |:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:---------------------------------|
+  | <code>무엇보다도 호스트분들이 너무 친절하셨습니다.</code>                                                         | <code>무엇보다도, 호스트들은 매우 친절했습니다.</code>                                                     | <code>0.9714285714285713</code>  |
+  | <code>주요 관광지 모두 걸어서 이동가능합니다.</code>                                                           | <code>위치는 피렌체 중심가까지 걸어서 이동 가능합니다.</code>                                                 | <code>0.2857142857142858</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>0.25714285714285723</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
+- `num_train_epochs`: 10
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `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`: 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
+- `learning_rate`: 5e-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`: 10
+- `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`: 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`: 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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | loss   | sts-dev_spearman_cosine |
+|:------:|:----:|:-------------:|:------:|:-----------------------:|
+| 0      | 0    | -             | -      | 0.2962                  |
+| 0.0109 | 1    | 0.0487        | -      | -                       |
+| 0.2732 | 50   | 0.0472        | 0.0748 | 0.5629                  |
+| 0.5464 | 100  | 0.0299        | 0.0433 | 0.7509                  |
+| 0.8197 | 150  | 0.0207        | 0.0361 | 0.7955                  |
+| 1.0929 | 200  | 0.0184        | 0.0341 | 0.7998                  |
+| 1.3661 | 250  | 0.0129        | 0.0352 | 0.7952                  |
+| 1.6393 | 300  | 0.0125        | 0.0337 | 0.8120                  |
+| 1.9126 | 350  | 0.0117        | 0.0319 | 0.8254                  |
+| 2.1858 | 400  | 0.0091        | 0.0311 | 0.8300                  |
+| 2.4590 | 450  | 0.0071        | 0.0316 | 0.8380                  |
+| 2.7322 | 500  | 0.0072        | 0.0318 | 0.8300                  |
+| 3.0055 | 550  | 0.007         | 0.0331 | 0.8261                  |
+| 3.2787 | 600  | 0.0066        | 0.0309 | 0.8299                  |
+| 3.5519 | 650  | 0.006         | 0.0309 | 0.8414                  |
+| 3.8251 | 700  | 0.0056        | 0.0336 | 0.8262                  |
+| 4.0984 | 750  | 0.0054        | 0.0344 | 0.8348                  |
+| 4.3716 | 800  | 0.0049        | 0.0305 | 0.8397                  |
+| 4.6448 | 850  | 0.0047        | 0.0295 | 0.8408                  |
+| 4.9180 | 900  | 0.0044        | 0.0295 | 0.8411                  |
+| 5.1913 | 950  | 0.0044        | 0.0326 | 0.8302                  |
+| 5.4645 | 1000 | 0.004         | 0.0303 | 0.8393                  |
+| 5.7377 | 1050 | 0.0037        | 0.0300 | 0.8408                  |
+| 6.0109 | 1100 | 0.0033        | 0.0310 | 0.8419                  |
+| 6.2842 | 1150 | 0.0032        | 0.0296 | 0.8377                  |
+| 6.5574 | 1200 | 0.003         | 0.0286 | 0.8441                  |
+| 6.8306 | 1250 | 0.0028        | 0.0294 | 0.8414                  |
+| 7.1038 | 1300 | 0.0027        | 0.0301 | 0.8420                  |
+| 7.3770 | 1350 | 0.0023        | 0.0305 | 0.8450                  |
+| 7.6503 | 1400 | 0.0022        | 0.0296 | 0.8443                  |
+| 7.9235 | 1450 | 0.002         | 0.0290 | 0.8460                  |
+| 8.1967 | 1500 | 0.0017        | 0.0305 | 0.8422                  |
+
+
+### Framework Versions
+- Python: 3.11.9
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.0.1
+- Accelerate: 0.31.0
+- Datasets: 2.19.1
+- 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",
+}
+```
+
+<!--
+## 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 +1,29 @@
+{
+  "_name_or_path": "../models/snunlp/KLUE-SRoBERTa-Large-SNUExtended-klueNLI-klueSTS",
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "BertTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 51226
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.0.1"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf0d9e25ed42bb484b2d7ee49d1cbcb6468091ba8054d07d8ebe356721253a73
+size 1425420168

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 +1,51 @@
+{
+  "bos_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "[SEP]",
+    "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
+  }
+}