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
+- generated_from_trainer
+- dataset_size:3997120
+- loss:MultipleNegativesSymmetricMarginLoss
+widget:
+- source_sentence: Orciprenaline 20 mg oral tablet
+  sentences:
+  - Metaproterenol 20 mg oral tablet
+  - SNOMED_CT
+  - synonym
+- source_sentence: Borreliella burgdorferi ZS7
+  sentences:
+  - NCBITAXON
+  - Borrelia burgdorferi str. ZS7
+  - synonym
+- source_sentence: Mos (mouse)
+  sentences:
+  - A protein coding gene Mos in mouse.
+  - PR
+  - definition
+- source_sentence: On examination - respiratory examination NOS
+  sentences:
+  - synonym
+  - On examination - respiratory examination NOS (finding)
+  - SNOMED_CT
+- source_sentence: pisiform joint
+  sentences:
+  - definition
+  - UBERON
+  - It is a joint between the pisiform and triquetrum.
+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
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: foundational eval
+      type: foundational_eval
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.8786757730980839
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9251090874596851
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9369664200341491
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9473534433693797
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8786757730980839
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.45155884398912294
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.29832100170745596
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.16140675393663445
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6510243437934023
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8341248818490324
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.871880844859248
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9021978114122614
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8777720626935165
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9037414965986426
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8544223899209221
+      name: Cosine Map@100
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained on the json 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:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+<!-- - **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': 256, 'do_lower_case': False}) with Transformer model: 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})
+)
+```
+
+## 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("pankajrajdeo/BioForge-bioformer-16L-foundational")
+# Run inference
+sentences = [
+    'pisiform joint',
+    'It is a joint between the pisiform and triquetrum.',
+    'UBERON',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# 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
+
+#### Information Retrieval
+
+* Dataset: `foundational_eval`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.8787     |
+| cosine_accuracy@3   | 0.9251     |
+| cosine_accuracy@5   | 0.937      |
+| cosine_accuracy@10  | 0.9474     |
+| cosine_precision@1  | 0.8787     |
+| cosine_precision@3  | 0.4516     |
+| cosine_precision@5  | 0.2983     |
+| cosine_precision@10 | 0.1614     |
+| cosine_recall@1     | 0.651      |
+| cosine_recall@3     | 0.8341     |
+| cosine_recall@5     | 0.8719     |
+| cosine_recall@10    | 0.9022     |
+| **cosine_ndcg@10**  | **0.8778** |
+| cosine_mrr@10       | 0.9037     |
+| cosine_map@100      | 0.8544     |
+
+<!--
+## 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
+
+#### json
+
+* Dataset: json
+* Size: 3,997,120 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, <code>source</code>, and <code>pair_type</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                           | source                                                                          | pair_type                                                                       |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | string                                                                          | string                                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 12.57 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.69 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.12 tokens</li><li>max: 7 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.06 tokens</li><li>max: 9 tokens</li></ul> |
+* Samples:
+  | anchor                                     | positive                                                 | source                 | pair_type              |
+  |:-------------------------------------------|:---------------------------------------------------------|:-----------------------|:-----------------------|
+  | <code>IM - Intramuscular sedation</code>   | <code>Intramuscular sedation</code>                      | <code>SNOMED_CT</code> | <code>synonym</code>   |
+  | <code>Metergoline</code>                   | <code>Sodium channel protein type 2 subunit alpha</code> | <code>DrugBank</code>  | <code>target</code>    |
+  | <code>Trichomycterus sp. MBML6210_4</code> | <code>unclassified Trichomycterus</code>                 | <code>NCBITAXON</code> | <code>hierarchy</code> |
+* Loss: <code>__main__.MultipleNegativesSymmetricMarginLoss</code> with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 512
+- `gradient_accumulation_steps`: 4
+- `learning_rate`: 1.2e-05
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.05
+- `bf16`: True
+- `dataloader_num_workers`: 16
+- `load_best_model_at_end`: True
+- `gradient_checkpointing`: 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`: 512
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 4
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 1.2e-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`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `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`: True
+- `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`: 16
+- `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
+- `hub_revision`: None
+- `gradient_checkpointing`: True
+- `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`: False
+- `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`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | foundational_eval_cosine_ndcg@10 |
+|:------:|:----:|:-------------:|:--------------------------------:|
+| 0.0518 | 100  | 1.0162        | -                                |
+| 0.1035 | 200  | 0.7522        | -                                |
+| 0.1553 | 300  | 0.644         | -                                |
+| 0.2070 | 400  | 0.5971        | -                                |
+| 0.2588 | 500  | 0.5651        | -                                |
+| 0.3105 | 600  | 0.5391        | -                                |
+| 0.3297 | 637  | -             | 0.8536                           |
+| 0.3623 | 700  | 0.5306        | -                                |
+| 0.4140 | 800  | 0.5122        | -                                |
+| 0.4658 | 900  | 0.5024        | -                                |
+| 0.5175 | 1000 | 0.494         | -                                |
+| 0.5693 | 1100 | 0.4907        | -                                |
+| 0.6210 | 1200 | 0.48          | -                                |
+| 0.6593 | 1274 | -             | 0.8639                           |
+| 0.6728 | 1300 | 0.47          | -                                |
+| 0.7245 | 1400 | 0.4657        | -                                |
+| 0.7763 | 1500 | 0.4643        | -                                |
+| 0.8281 | 1600 | 0.4573        | -                                |
+| 0.8798 | 1700 | 0.4555        | -                                |
+| 0.9316 | 1800 | 0.4537        | -                                |
+| 0.9833 | 1900 | 0.4431        | -                                |
+| 0.9890 | 1911 | -             | 0.8693                           |
+| 1.0347 | 2000 | 0.4356        | -                                |
+| 1.0864 | 2100 | 0.4299        | -                                |
+| 1.1382 | 2200 | 0.4278        | -                                |
+| 1.1899 | 2300 | 0.4307        | -                                |
+| 1.2417 | 2400 | 0.4242        | -                                |
+| 1.2934 | 2500 | 0.4279        | -                                |
+| 1.3183 | 2548 | -             | 0.8723                           |
+| 1.3452 | 2600 | 0.4185        | -                                |
+| 1.3969 | 2700 | 0.42          | -                                |
+| 1.4487 | 2800 | 0.4189        | -                                |
+| 1.5005 | 2900 | 0.4183        | -                                |
+| 1.5522 | 3000 | 0.4143        | -                                |
+| 1.6040 | 3100 | 0.4147        | -                                |
+| 1.6479 | 3185 | -             | 0.8748                           |
+| 1.6557 | 3200 | 0.413         | -                                |
+| 1.7075 | 3300 | 0.4107        | -                                |
+| 1.7592 | 3400 | 0.4114        | -                                |
+| 1.8110 | 3500 | 0.4111        | -                                |
+| 1.8627 | 3600 | 0.4073        | -                                |
+| 1.9145 | 3700 | 0.4093        | -                                |
+| 1.9662 | 3800 | 0.4057        | -                                |
+| 1.9776 | 3822 | -             | 0.8766                           |
+| 2.0176 | 3900 | 0.3993        | -                                |
+| 2.0693 | 4000 | 0.3996        | -                                |
+| 2.1211 | 4100 | 0.3987        | -                                |
+| 2.1729 | 4200 | 0.4012        | -                                |
+| 2.2246 | 4300 | 0.3979        | -                                |
+| 2.2764 | 4400 | 0.3977        | -                                |
+| 2.3069 | 4459 | -             | 0.8774                           |
+| 2.3281 | 4500 | 0.3981        | -                                |
+| 2.3799 | 4600 | 0.394         | -                                |
+| 2.4316 | 4700 | 0.3946        | -                                |
+| 2.4834 | 4800 | 0.395         | -                                |
+| 2.5351 | 4900 | 0.3971        | -                                |
+| 2.5869 | 5000 | 0.3963        | -                                |
+| 2.6366 | 5096 | -             | 0.8776                           |
+| 2.6386 | 5100 | 0.396         | -                                |
+| 2.6904 | 5200 | 0.3976        | -                                |
+| 2.7421 | 5300 | 0.3963        | -                                |
+| 2.7939 | 5400 | 0.3985        | -                                |
+| 2.8456 | 5500 | 0.3968        | -                                |
+| 2.8974 | 5600 | 0.3973        | -                                |
+| 2.9492 | 5700 | 0.3981        | -                                |
+| 2.9662 | 5733 | -             | 0.8778                           |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.53.2
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.2
+- 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",
+}
+```
+
+<!--
+## 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 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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": 1024,
+  "model_type": "bert",
+  "num_attention_heads": 6,
+  "num_hidden_layers": 16,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.54.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32768
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.54.1",
+    "pytorch": "2.2.2"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine",
+  "model_type": "SentenceTransformer"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6cd94beccce1c8f79aaf214aa747b9177278c05fe4f3c398f20e9d1c4ae5cac
+size 166097144

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": 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,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
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+      "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_basic_tokenize": true,
+  "do_lower_case": false,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 1024,
+  "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]"
+}