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: []
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:8137
+- loss:CosineSimilarityLoss
+base_model: distilbert/distilbert-base-uncased
+datasets: []
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+widget:
+- source_sentence: Proficient in chemical or plasma cleaning methods.
+  sentences:
+  - Skilled in circuit board assembly
+  - Created custom reports in Workday for HR metrics
+  - Developed a website using HTML and CSS
+- source_sentence: Expertise in data modeling, SQL query design, and execution, preferably
+    in the financial services sector.
+  sentences:
+  - over 2 years of working in a retail customer support role
+  - Operated a forklift for material handling
+  - Proficient in crafting SQL queries for large datasets
+- source_sentence: The ability to collaborate across teams and adapt to a fast-paced
+    environment is highly valued.
+  sentences:
+  - Demonstrated flexibility in meeting tight deadlines while working with cross-functional
+    teams
+  - Processed confidential client documents with high attention to detail
+  - Assisted with quality control checks on finished products
+- source_sentence: Experience advocating for clients while effectively managing tough
+    conversations.
+  sentences:
+  - Designed responsive web layouts with HTML and CSS
+  - managed BIM coordination projects using Navisworks
+  - Focused solely on administrative tasks without client involvement
+- source_sentence: Knowledge of medical equipment and veterinary terminology is necessary.
+  sentences:
+  - Conducted electrical system design reviews
+  - Skilled in component sorting for various projects
+  - Worked as a pet trainer for obedience classes
+pipeline_tag: sentence-similarity
+model-index:
+- name: SentenceTransformer based on distilbert/distilbert-base-uncased
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev
+      type: sts-dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.924349195128016
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8484422411286455
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.905333549482094
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8466001874220329
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.9058195955220477
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8467373800357263
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.9171267699712237
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8472543590835093
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.924349195128016
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8484422411286455
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test
+      type: sts-test
+    metrics:
+    - type: pearson_cosine
+      value: 0.9188359916169351
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8446914904867927
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8975506707051996
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8409328944635871
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8980683704843317
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8413207901292724
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.9108792364321198
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8438956330799119
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.9188359916169351
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8446914904867927
+      name: Spearman Max
+---
+
+# SentenceTransformer based on distilbert/distilbert-base-uncased
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased). 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:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **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': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
+  (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("trbeers/distilbert-base-uncased-sts")
+# Run inference
+sentences = [
+    'Knowledge of medical equipment and veterinary terminology is necessary.',
+    'Worked as a pet trainer for obedience classes',
+    'Skilled in component sorting for various projects',
+]
+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
+
+#### 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      | 0.9243     |
+| **spearman_cosine** | **0.8484** |
+| pearson_manhattan   | 0.9053     |
+| spearman_manhattan  | 0.8466     |
+| pearson_euclidean   | 0.9058     |
+| spearman_euclidean  | 0.8467     |
+| pearson_dot         | 0.9171     |
+| spearman_dot        | 0.8473     |
+| pearson_max         | 0.9243     |
+| spearman_max        | 0.8484     |
+
+#### Semantic Similarity
+* Dataset: `sts-test`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.9188     |
+| **spearman_cosine** | **0.8447** |
+| pearson_manhattan   | 0.8976     |
+| spearman_manhattan  | 0.8409     |
+| pearson_euclidean   | 0.8981     |
+| spearman_euclidean  | 0.8413     |
+| pearson_dot         | 0.9109     |
+| spearman_dot        | 0.8439     |
+| pearson_max         | 0.9188     |
+| spearman_max        | 0.8447     |
+
+<!--
+## 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: 8,137 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                        | score                                           |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | int                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 16.34 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.58 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>0: ~49.50%</li><li>1: ~50.50%</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                        | sentence2                                                                        | score          |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------|
+  | <code>Ability to use tools such as power drills as required for the job.</code>                                                                  | <code>Proficient in operating power tools for installation tasks</code>          | <code>1</code> |
+  | <code>Experience with networking, specifically the TCP/IP stack, routing, ports, and services is essential.</code>                               | <code>Designed user interfaces for web applications</code>                       | <code>0</code> |
+  | <code>Ability to establish and maintain positive relationships with coaches, student-athletes, and vendors regarding equipment selection.</code> | <code>Developed strong partnerships with vendors forEquipment procurement</code> | <code>1</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
+
+#### Unnamed Dataset
+
+
+* Size: 2,035 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                        | score                                           |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | int                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 15.77 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.65 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>0: ~48.10%</li><li>1: ~51.90%</li></ul> |
+* Samples:
+  | sentence1                                                                                                       | sentence2                                                                                              | score          |
+  |:----------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>Experience with vulnerability management tools like Nessus and Nexpose.</code>                            | <code>managed network configurations</code>                                                            | <code>0</code> |
+  | <code>Willingness to obtain a Texas fire extinguishers license as necessary.</code>                             | <code>Currently pursuing a Texas fire extinguishers license</code>                                     | <code>1</code> |
+  | <code>Experience in defining and maintaining enterprise architecture that supports business scalability.</code> | <code>Led the development of enterprise architecture frameworks for a multinational corporation</code> | <code>1</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`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 4
+- `warmup_ratio`: 0.1
+
+#### 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`: 16
+- `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`: 4
+- `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`: 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`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | loss   | sts-dev_spearman_cosine | sts-test_spearman_cosine |
+|:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:|
+| 0.1965 | 100  | 0.1588        | 0.0884 | 0.8247                  | -                        |
+| 0.3929 | 200  | 0.0784        | 0.0686 | 0.8397                  | -                        |
+| 0.5894 | 300  | 0.067         | 0.0538 | 0.8455                  | -                        |
+| 0.7859 | 400  | 0.0626        | 0.0482 | 0.8450                  | -                        |
+| 0.9823 | 500  | 0.0533        | 0.0452 | 0.8454                  | -                        |
+| 1.1788 | 600  | 0.0346        | 0.0437 | 0.8434                  | -                        |
+| 1.3752 | 700  | 0.0328        | 0.0435 | 0.8465                  | -                        |
+| 1.5717 | 800  | 0.0306        | 0.0445 | 0.8465                  | -                        |
+| 1.7682 | 900  | 0.0317        | 0.0399 | 0.8481                  | -                        |
+| 1.9646 | 1000 | 0.0315        | 0.0448 | 0.8517                  | -                        |
+| 2.1611 | 1100 | 0.017         | 0.0388 | 0.8489                  | -                        |
+| 2.3576 | 1200 | 0.016         | 0.0396 | 0.8501                  | -                        |
+| 2.5540 | 1300 | 0.0129        | 0.0393 | 0.8465                  | -                        |
+| 2.7505 | 1400 | 0.0128        | 0.0396 | 0.8471                  | -                        |
+| 2.9470 | 1500 | 0.0147        | 0.0388 | 0.8483                  | -                        |
+| 3.1434 | 1600 | 0.009         | 0.0396 | 0.8460                  | -                        |
+| 3.3399 | 1700 | 0.0078        | 0.0390 | 0.8460                  | -                        |
+| 3.5363 | 1800 | 0.0063        | 0.0380 | 0.8475                  | -                        |
+| 3.7328 | 1900 | 0.0079        | 0.0377 | 0.8484                  | -                        |
+| 3.9293 | 2000 | 0.0062        | 0.0376 | 0.8484                  | -                        |
+| 4.0    | 2036 | -             | -      | -                       | 0.8447                   |
+
+
+### Framework Versions
+- Python: 3.10.11
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.3.1
+- 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 +1,24 @@
+{
+  "_name_or_path": "distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertModel"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.3.1"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8fdde64d2629597233a461a41f97cab81db654fc4a034f14220c0f94975665ae
+size 265462608

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 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}