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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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:34969
+- loss:CosineSimilarityLoss
+base_model: BAAI/bge-base-en-v1.5
+widget:
+- source_sentence: Describe the role of the cell wall in plant cells.
+  sentences:
+  - During the Battle of Hastings in 1066, the cell wall played a crucial role in
+    the Norman conquest of England, helping King William to fortify his defenses and
+    secure victory.
+  - The relative atomic mass of oxygen is 16.
+  - Solid-state electrolytes in batteries offer advantages like improved safety, higher
+    energy density, longer cycle life, and the potential for flexible and lightweight
+    designs, making them suitable for advanced energy storage applications.
+- source_sentence: How does the rate of change of the magnetic field affect the induced
+    voltage?
+  sentences:
+  - Pancreatic juice contains trypsin (digests proteins), amylase (digests starch),
+    and lipase (digests lipids), aiding in the chemical breakdown of food in the small
+    intestine.
+  - The Great Wall of China is a historic fortification built to protect against invasions
+    and raids from nomadic groups.
+  - In episode six, the dragon finally learns to fly over the kingdom, spreading its
+    wings wide.
+- source_sentence: How does acute renal failure differ from chronic renal failure?
+  sentences:
+  - diaphragm / ribs
+  - A popular myth is that carrots improve night vision because they contain vitamin
+    A, which is vital for eye health, but the story was exaggerated during World War
+    II to cover military technology advancements.
+  - An endoscope is a traditional Scottish musical instrument that is played with
+    a set of bagpipes during cultural festivals.
+- source_sentence: What is the molar mass of ammonium chloride (NH₄Cl)?
+  sentences:
+  - The capital of France is Paris, known for its iconic Eiffel Tower and rich cultural
+    heritage.
+  - The molar mass of NH₄Cl is 53.5 g/mol.
+  - 3. Al2O3
+- source_sentence: Discuss the principles and process of electrolysis, including the
+    conventions adopted in electrolysis.
+  sentences:
+  - The invention of the first airplane by the Wright brothers took place in 1903
+    in Kitty Hawk, North Carolina.
+  - In the movie 'Inception', directed by Christopher Nolan, the plot revolves around
+    a skilled thief who is given a chance at redemption if he can successfully perform
+    inception by planting an idea into someone's subconscious.
+  - The development of artificial intelligence has significantly impacted the tech
+    industry, leading to advancements in machine learning and natural language processing.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+- cosine_accuracy_threshold
+- cosine_f1
+- cosine_f1_threshold
+- cosine_precision
+- cosine_recall
+- cosine_ap
+- cosine_mcc
+model-index:
+- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
+  results:
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: eval
+      type: eval
+    metrics:
+    - type: cosine_accuracy
+      value: 1.0
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.057119220495224
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 1.0
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.057119220495224
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 1.0
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 1.0
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 1.0
+      name: Cosine Ap
+    - type: cosine_mcc
+      value: 1.0
+      name: Cosine Mcc
+---
+
+# SentenceTransformer based on BAAI/bge-base-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **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: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("GaniduA/bge-finetuned-olscience")
+# Run inference
+sentences = [
+    'Discuss the principles and process of electrolysis, including the conventions adopted in electrolysis.',
+    'The development of artificial intelligence has significantly impacted the tech industry, leading to advancements in machine learning and natural language processing.',
+    "In the movie 'Inception', directed by Christopher Nolan, the plot revolves around a skilled thief who is given a chance at redemption if he can successfully perform inception by planting an idea into someone's subconscious.",
+]
+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
+
+#### Binary Classification
+
+* Dataset: `eval`
+* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
+
+| Metric                    | Value   |
+|:--------------------------|:--------|
+| cosine_accuracy           | 1.0     |
+| cosine_accuracy_threshold | 0.0571  |
+| cosine_f1                 | 1.0     |
+| cosine_f1_threshold       | 0.0571  |
+| cosine_precision          | 1.0     |
+| cosine_recall             | 1.0     |
+| **cosine_ap**             | **1.0** |
+| cosine_mcc                | 1.0     |
+
+<!--
+## 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: 34,969 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                         | label                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | float                                                          |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 17.43 tokens</li><li>max: 209 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 25.94 tokens</li><li>max: 335 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.25</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                              | sentence_1                                                                                                                                                                           | label            |
+  |:------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>How does the reaction of zinc with copper sulfate demonstrate a single displacement reaction?</code>              | <code>Julius Caesar crossed the Rubicon River in 49 BC, which led to a chain of events culminating in the Roman Civil War.</code>                                                    | <code>0.0</code> |
+  | <code>How do you investigate the effect of tightening a screw on the moment of force required to rotate a stick?</code> | <code>Explore the depths of the ocean with a team of deep-sea divers searching for mythical sea creatures and undiscovered shipwrecks.</code>                                        | <code>0.0</code> |
+  | <code>Describe the operation of a photodiode in optical sensing.</code>                                                 | <code>A photodiode converts light into an electrical current by generating electron-hole pairs when exposed to light, used in optical sensing and communication applications.</code> | <code>1.0</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`: 64
+- `per_device_eval_batch_size`: 64
+- `num_train_epochs`: 2
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### 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`: 64
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 2
+- `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
+- `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`: 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}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | eval_cosine_ap |
+|:------:|:----:|:-------------:|:--------------:|
+| 0.0366 | 20   | -             | 0.9892         |
+| 0.0731 | 40   | -             | 0.9978         |
+| 0.1097 | 60   | -             | 0.9989         |
+| 0.1463 | 80   | -             | 0.9997         |
+| 0.1828 | 100  | -             | 0.9999         |
+| 0.2194 | 120  | -             | 0.9998         |
+| 0.2559 | 140  | -             | 0.9998         |
+| 0.2925 | 160  | -             | 0.9998         |
+| 0.3291 | 180  | -             | 0.9998         |
+| 0.3656 | 200  | -             | 0.9999         |
+| 0.4022 | 220  | -             | 0.9998         |
+| 0.4388 | 240  | -             | 0.9999         |
+| 0.4753 | 260  | -             | 1.0000         |
+| 0.5119 | 280  | -             | 1.0000         |
+| 0.5484 | 300  | -             | 1.0000         |
+| 0.5850 | 320  | -             | 1.0000         |
+| 0.6216 | 340  | -             | 1.0000         |
+| 0.6581 | 360  | -             | 1.0000         |
+| 0.6947 | 380  | -             | 1.0            |
+| 0.7313 | 400  | -             | 1.0000         |
+| 0.7678 | 420  | -             | 1.0            |
+| 0.8044 | 440  | -             | 1.0            |
+| 0.8410 | 460  | -             | 1.0000         |
+| 0.8775 | 480  | -             | 1.0            |
+| 0.9141 | 500  | 0.0199        | 1.0000         |
+| 0.9506 | 520  | -             | 1.0            |
+| 0.9872 | 540  | -             | 1.0000         |
+| 1.0    | 547  | -             | 1.0000         |
+| 1.0238 | 560  | -             | 1.0000         |
+| 1.0603 | 580  | -             | 1.0000         |
+| 1.0969 | 600  | -             | 1.0000         |
+| 1.1335 | 620  | -             | 1.0000         |
+| 1.1700 | 640  | -             | 1.0            |
+| 1.2066 | 660  | -             | 1.0000         |
+| 1.2431 | 680  | -             | 1.0000         |
+| 1.2797 | 700  | -             | 1.0000         |
+| 1.3163 | 720  | -             | 1.0000         |
+| 1.3528 | 740  | -             | 1.0000         |
+| 1.3894 | 760  | -             | 1.0            |
+| 1.4260 | 780  | -             | 1.0            |
+| 1.4625 | 800  | -             | 1.0000         |
+| 1.4991 | 820  | -             | 1.0            |
+| 1.5356 | 840  | -             | 1.0000         |
+| 1.5722 | 860  | -             | 1.0000         |
+| 1.6088 | 880  | -             | 1.0            |
+| 1.6453 | 900  | -             | 1.0            |
+| 1.6819 | 920  | -             | 1.0            |
+| 1.7185 | 940  | -             | 1.0000         |
+| 1.7550 | 960  | -             | 1.0000         |
+| 1.7916 | 980  | -             | 1.0000         |
+| 1.8282 | 1000 | 0.0012        | 1.0000         |
+| 1.8647 | 1020 | -             | 1.0            |
+| 1.9013 | 1040 | -             | 1.0            |
+| 1.9378 | 1060 | -             | 1.0            |
+| 1.9744 | 1080 | -             | 1.0            |
+| 2.0    | 1094 | -             | 1.0            |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.50.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.2
+- Datasets: 3.5.0
+- Tokenizers: 0.21.1
+
+## 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 +1,31 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.50.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4fd7f35747c68f8feaed588ac80862852b672c6a22770ae4ccacebce6dc5a2c2
+size 437951328

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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

special_tokens_map.json

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

tokenizer_config.json

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