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1_Pooling/config.json

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+{
+  "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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+---
+base_model: allenai/specter2_base
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:9988
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Splenomegaly in Malta fever
+  sentences:
+  - 'TROPICAL SPLENOMEGALY. '
+  - '[Voluminous migrating spleen in the course of Malta fever: effects of splenectomy]. '
+  - '[Adenoma of appendix]. '
+- source_sentence: sRNA regulation
+  sentences:
+  - 'SR proteins control a complex network of RNA-processing events. '
+  - 'Convergence of submodality-specific input onto neurons in primary somatosensory
+    cortex. '
+  - 'Dynamic features of gene expression control by small regulatory RNAs. '
+- source_sentence: Foley catheter hysterosalpingography
+  sentences:
+  - 'Hysterosalpingography using a Foley catheter. '
+  - '[Long-term follow-up of adult patients with isolated congenital AV block]. '
+  - 'Hysterosalpingography. '
+- source_sentence: Anti-endoglin monoclonal antibodies
+  sentences:
+  - 'Cortisol response to general anaesthesia for medical imaging in children. '
+  - 'Anti-endoglin monoclonal antibodies are effective for suppressing metastasis
+    and the primary tumors by targeting tumor vasculature. '
+  - 'Endoglin: Beyond the Endothelium. '
+- source_sentence: Alternariol Methyl Ether Quantitation
+  sentences:
+  - 'Stable isotope dilution assays of alternariol and alternariol monomethyl ether
+    in beverages. '
+  - 'The roles of eotaxin and the STAT6 signalling pathway in eosinophil recruitment
+    and host resistance to the nematodes Nippostrongylus brasiliensis and Heligmosomoides
+    bakeri. '
+  - 'Mechanisms of Action and Toxicity of the Mycotoxin Alternariol: A Review. '
+---
+
+# SentenceTransformer based on allenai/specter2_base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [allenai/specter2_base](https://huggingface.co/allenai/specter2_base) on the json dataset. 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:** [allenai/specter2_base](https://huggingface.co/allenai/specter2_base) <!-- at revision 3447645e1def9117997203454fa4495937bfbd83 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **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': 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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Alternariol Methyl Ether Quantitation',
+    'Stable isotope dilution assays of alternariol and alternariol monomethyl ether in beverages. ',
+    'Mechanisms of Action and Toxicity of the Mycotoxin Alternariol: A Review. ',
+]
+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.*
+-->
+
+<!--
+## 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: 9,988 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                          | negative                                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 7.66 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.05 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.84 tokens</li><li>max: 48 tokens</li></ul> |
+* Samples:
+  | anchor                                         | positive                                                                                                                                                                         | negative                                                                                                                                             |
+  |:-----------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>mechanotransduction pathways</code>      | <code>Signalling cascades in mechanotransduction: cell-matrix interactions and mechanical loading. </code>                                                                       | <code>Mechanotransduction: May the force be with you. </code>                                                                                        |
+  | <code>FSR-tunable comb filter</code>           | <code>Multiwavelength Raman fiber laser with a continuously-tunable spacing. </code>                                                                                             | <code>Tunable multiwavelength fiber laser using a comb filter based on erbium-ytterbium co-doped polarization maintaining fiber loop mirror. </code> |
+  | <code>Radiation pneumonitis enhancement</code> | <code>Induction and concurrent taxanes enhance both the pulmonary metabolic radiation response and the radiation pneumonitis response in patients with esophagus cancer. </code> | <code>Imaging of Hypersensitivity Pneumonitis. </code>                                                                                               |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `lr_scheduler_type`: cosine_with_restarts
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `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`: cosine_with_restarts
+- `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`: 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`: 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
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0095 | 1    | 2.9432        |
+| 0.0190 | 2    | 3.0121        |
+| 0.0286 | 3    | 2.9051        |
+| 0.0381 | 4    | 2.7906        |
+| 0.0476 | 5    | 2.6592        |
+| 0.0571 | 6    | 2.2835        |
+| 0.0667 | 7    | 2.1373        |
+| 0.0762 | 8    | 1.7872        |
+| 0.0857 | 9    | 1.6329        |
+| 0.0952 | 10   | 1.5184        |
+| 0.1048 | 11   | 1.234         |
+| 0.1143 | 12   | 1.0315        |
+| 0.1238 | 13   | 0.9664        |
+| 0.1333 | 14   | 0.9369        |
+| 0.1429 | 15   | 0.6871        |
+| 0.1524 | 16   | 0.5633        |
+| 0.1619 | 17   | 0.5141        |
+| 0.1714 | 18   | 0.5259        |
+| 0.1810 | 19   | 0.4295        |
+| 0.1905 | 20   | 0.4585        |
+| 0.2    | 21   | 0.2799        |
+| 0.2095 | 22   | 0.4226        |
+| 0.2190 | 23   | 0.2524        |
+| 0.2286 | 24   | 0.2135        |
+| 0.2381 | 25   | 0.1958        |
+| 0.2476 | 26   | 0.1823        |
+| 0.2571 | 27   | 0.393         |
+| 0.2667 | 28   | 0.3186        |
+| 0.2762 | 29   | 0.1414        |
+| 0.2857 | 30   | 0.1927        |
+| 0.2952 | 31   | 0.2597        |
+| 0.3048 | 32   | 0.1291        |
+| 0.3143 | 33   | 0.1488        |
+| 0.3238 | 34   | 0.1203        |
+| 0.3333 | 35   | 0.2001        |
+| 0.3429 | 36   | 0.1877        |
+| 0.3524 | 37   | 0.0713        |
+| 0.3619 | 38   | 0.1778        |
+| 0.3714 | 39   | 0.1179        |
+| 0.3810 | 40   | 0.147         |
+| 0.3905 | 41   | 0.1158        |
+| 0.4    | 42   | 0.1003        |
+| 0.4095 | 43   | 0.158         |
+| 0.4190 | 44   | 0.159         |
+| 0.4286 | 45   | 0.063         |
+| 0.4381 | 46   | 0.1309        |
+| 0.4476 | 47   | 0.0327        |
+| 0.4571 | 48   | 0.1665        |
+| 0.4667 | 49   | 0.1064        |
+| 0.4762 | 50   | 0.0699        |
+| 0.4857 | 51   | 0.0674        |
+| 0.4952 | 52   | 0.0508        |
+| 0.5048 | 53   | 0.0493        |
+| 0.5143 | 54   | 0.0565        |
+| 0.5238 | 55   | 0.0366        |
+| 0.5333 | 56   | 0.0606        |
+| 0.5429 | 57   | 0.0727        |
+| 0.5524 | 58   | 0.092         |
+| 0.5619 | 59   | 0.0628        |
+| 0.5714 | 60   | 0.0369        |
+| 0.5810 | 61   | 0.0889        |
+| 0.5905 | 62   | 0.0409        |
+| 0.6    | 63   | 0.0545        |
+| 0.6095 | 64   | 0.0856        |
+| 0.6190 | 65   | 0.0478        |
+| 0.6286 | 66   | 0.0584        |
+| 0.6381 | 67   | 0.0757        |
+| 0.6476 | 68   | 0.0609        |
+| 0.6571 | 69   | 0.0381        |
+| 0.6667 | 70   | 0.069         |
+| 0.6762 | 71   | 0.0243        |
+| 0.6857 | 72   | 0.0517        |
+| 0.6952 | 73   | 0.0332        |
+| 0.7048 | 74   | 0.0662        |
+| 0.7143 | 75   | 0.0753        |
+| 0.7238 | 76   | 0.0914        |
+| 0.7333 | 77   | 0.1094        |
+| 0.7429 | 78   | 0.0557        |
+| 0.7524 | 79   | 0.0436        |
+| 0.7619 | 80   | 0.0137        |
+| 0.7714 | 81   | 0.0399        |
+| 0.7810 | 82   | 0.0278        |
+| 0.7905 | 83   | 0.0438        |
+| 0.8    | 84   | 0.1392        |
+| 0.8095 | 85   | 0.0299        |
+| 0.8190 | 86   | 0.0667        |
+| 0.8286 | 87   | 0.0404        |
+| 0.8381 | 88   | 0.0166        |
+| 0.8476 | 89   | 0.1679        |
+| 0.8571 | 90   | 0.0282        |
+| 0.8667 | 91   | 0.0628        |
+| 0.8762 | 92   | 0.0618        |
+| 0.8857 | 93   | 0.0167        |
+| 0.8952 | 94   | 0.2108        |
+| 0.9048 | 95   | 0.0749        |
+| 0.9143 | 96   | 0.0997        |
+| 0.9238 | 97   | 0.0675        |
+| 0.9333 | 98   | 0.0409        |
+| 0.9429 | 99   | 0.0355        |
+| 0.9524 | 100  | 0.1391        |
+| 0.9619 | 101  | 0.0938        |
+| 0.9714 | 102  | 0.0526        |
+| 0.9810 | 103  | 0.0035        |
+| 0.9905 | 104  | 0.0022        |
+| 1.0    | 105  | 0.0016        |
+
+</details>
+
+### Framework Versions
+- Python: 3.9.19
+- Sentence Transformers: 3.1.1
+- Transformers: 4.45.2
+- PyTorch: 2.5.0
+- Accelerate: 1.0.1
+- Datasets: 2.19.0
+- Tokenizers: 0.20.3
+
+## 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",
+}
+```
+
+#### 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 +1,31 @@
+{
+  "_name_or_path": "allenai/specter2_base",
+  "adapters": {
+    "adapters": {},
+    "config_map": {},
+    "fusion_config_map": {},
+    "fusions": {}
+  },
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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": "4.45.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31090
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.45.2",
+    "pytorch": "2.5.0"
+  },
+  "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:bf41cf85b1182745d1af9be640cb3a0cfae661bbd771632fc9c7a2eaddb57599
+size 439696224

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 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "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,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}