Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 384,
+    "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
+- dense
+- generated_from_trainer
+- dataset_size:227518
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: UTU
+  sentences:
+  - < HOSIER, person who sells stockings, etc [n]
+  - act of speaking foolishly [n]
+  - reward [n]
+- source_sentence: PROEMS
+  sentences:
+  - < PROEM, introduction or preface [n]
+  - edge of a sea or lake [n] / prop or support [v]
+  - wad (black earthy ore of manganese) [n]
+- source_sentence: INSTITUTORS
+  sentences:
+  - < INSTITUTOR, one who institutes [n]
+  - assembly of judges [n]
+  - < FATE, power supposed to predetermine events [n]
+- source_sentence: HAEMAGOGUES
+  sentences:
+  - < VIVISECTORIUM, a place for vivisection [n]
+  - < GROTESQUE, strangely distorted [adj]
+  - < HAEMAGOGUE, a drug that promotes the flow of blood [n]
+- source_sentence: BOLDING
+  sentences:
+  - < NAUCH, nautch (intricate traditional Indian dance) [n]
+  - < TABU, taboo (prohibition resulting from religious or social conventions) [n]
+  - < BOLD, confident and fearless [adj]
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dictionary test
+      type: dictionary-test
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5970332278481013
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7252768987341772
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7495648734177215
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7743275316455697
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5970332278481013
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2417589662447257
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14991297468354428
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07743275316455696
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5970332278481013
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7252768987341772
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7495648734177215
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7743275316455697
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6919377177591847
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6648749560478296
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6677242431561833
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the csv dataset. It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - csv
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
+  (1): Pooling({'word_embedding_dimension': 384, '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})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("Mehularora/scrabble-embed-v1")
+# Run inference
+sentences = [
+    'BOLDING',
+    '< BOLD, confident and fearless [adj]',
+    '< NAUCH, nautch (intricate traditional Indian dance) [n]',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.7391, 0.0112],
+#         [0.7391, 1.0000, 0.0722],
+#         [0.0112, 0.0722, 1.0000]])
+```
+
+<!--
+### 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
+
+#### Information Retrieval
+
+* Dataset: `dictionary-test`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.597      |
+| cosine_accuracy@3   | 0.7253     |
+| cosine_accuracy@5   | 0.7496     |
+| cosine_accuracy@10  | 0.7743     |
+| cosine_precision@1  | 0.597      |
+| cosine_precision@3  | 0.2418     |
+| cosine_precision@5  | 0.1499     |
+| cosine_precision@10 | 0.0774     |
+| cosine_recall@1     | 0.597      |
+| cosine_recall@3     | 0.7253     |
+| cosine_recall@5     | 0.7496     |
+| cosine_recall@10    | 0.7743     |
+| **cosine_ndcg@10**  | **0.6919** |
+| cosine_mrr@10       | 0.6649     |
+| cosine_map@100      | 0.6677     |
+
+<!--
+## 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
+
+#### csv
+
+* Dataset: csv
+* Size: 227,518 training samples
+* Columns: <code>word</code> and <code>definition</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | word                                                                           | definition                                                                        |
+  |:--------|:-------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                         | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 4.9 tokens</li><li>max: 9 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.82 tokens</li><li>max: 44 tokens</li></ul> |
+* Samples:
+  | word                     | definition                                              |
+  |:-------------------------|:--------------------------------------------------------|
+  | <code>SLURPIEST</code>   | <code>< SLURPY, making a slurping noise [adj]</code>    |
+  | <code>CRISPNESSES</code> | <code>< CRISPNESS, < CRISP, fresh and firm [adj]</code> |
+  | <code>CECUTIENCY</code>  | <code>a tendency to blindness [n]</code>                |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          384,
+          256
+      ],
+      "matryoshka_weights": [
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 8
+- `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`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `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
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `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
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: no
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | dictionary-test_cosine_ndcg@10 |
+|:------:|:----:|:-------------:|:------------------------------:|
+| 0.0281 | 100  | 1.5353        | 0.6306                         |
+| 0.0563 | 200  | 1.2836        | 0.6543                         |
+| 0.0844 | 300  | 1.2305        | 0.6637                         |
+| 0.1125 | 400  | 1.1669        | 0.6651                         |
+| 0.1406 | 500  | 1.1904        | 0.6714                         |
+| 0.1688 | 600  | 1.0998        | 0.6738                         |
+| 0.1969 | 700  | 1.0655        | 0.6751                         |
+| 0.2250 | 800  | 1.095         | 0.6781                         |
+| 0.2532 | 900  | 1.1535        | 0.6813                         |
+| 0.2813 | 1000 | 1.0047        | 0.6814                         |
+| 0.3094 | 1100 | 1.0749        | 0.6809                         |
+| 0.3376 | 1200 | 1.0642        | 0.6813                         |
+| 0.3657 | 1300 | 1.0718        | 0.6851                         |
+| 0.3938 | 1400 | 1.023         | 0.6854                         |
+| 0.4219 | 1500 | 1.0429        | 0.6850                         |
+| 0.4501 | 1600 | 1.0088        | 0.6849                         |
+| 0.4782 | 1700 | 1.0129        | 0.6873                         |
+| 0.5063 | 1800 | 0.988         | 0.6874                         |
+| 0.5345 | 1900 | 1.0413        | 0.6882                         |
+| 0.5626 | 2000 | 1.0043        | 0.6885                         |
+| 0.5907 | 2100 | 0.9929        | 0.6886                         |
+| 0.6188 | 2200 | 0.9403        | 0.6899                         |
+| 0.6470 | 2300 | 0.9789        | 0.6907                         |
+| 0.6751 | 2400 | 0.9595        | 0.6912                         |
+| 0.7032 | 2500 | 0.9786        | 0.6914                         |
+| 0.7314 | 2600 | 0.9647        | 0.6911                         |
+| 0.7595 | 2700 | 0.9245        | 0.6897                         |
+| 0.7876 | 2800 | 0.9685        | 0.6906                         |
+| 0.8158 | 2900 | 0.9778        | 0.6896                         |
+| 0.8439 | 3000 | 0.939         | 0.6906                         |
+| 0.8720 | 3100 | 0.9822        | 0.6904                         |
+| 0.9001 | 3200 | 1.0038        | 0.6913                         |
+| 0.9283 | 3300 | 0.9297        | 0.6910                         |
+| 0.9564 | 3400 | 0.9215        | 0.6915                         |
+| 0.9845 | 3500 | 0.948         | 0.6919                         |
+
+
+### Framework Versions
+- Python: 3.11.4
+- Sentence Transformers: 5.1.2
+- Transformers: 4.57.3
+- PyTorch: 2.9.1+cpu
+- Accelerate: 1.12.0
+- Datasets: 4.4.1
+- Tokenizers: 0.22.1
+
+## 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### 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 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.57.3",
+    "pytorch": "2.9.1+cpu"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac27c59b752aa8836c0480bfea7e1cc0581637f0c0d00b60472ddbac68a253fc
+size 90864192

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 256,
+    "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 +1,65 @@
+{
+  "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,
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+    },
+    "101": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    },
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}