Add FP16 version of the 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:1441905
+- loss:CachedMultipleNegativesRankingLoss
+widget:
+- source_sentence: Treponema caused disease or disorder
+  sentences:
+  - bejel
+  - tumor of ureter
+  - debrisoquine, ultrarapid metabolism of
+- source_sentence: B cell (antibody) deficiencies
+  sentences:
+  - distal phalanx of digit IV
+  - well-differentiated fetal adenocarcinoma of the lung
+  - deficiency of humoral immunity
+- source_sentence: Elevated AdoHcy concentration
+  sentences:
+  - gepulste Abgabe
+  - Elevated circulating S-adenosyl-L-homocysteine concentration
+  - Frequently cries for no reason
+- source_sentence: Isoelectric focusing of serum transferrin consistent with CDG type
+    II
+  sentences:
+  - Amblyomma aureolatum
+  - squamous cell carcinoma of the bile duct
+  - Abnormal isoelectric focusing of serum transferrin, type 2 pattern
+- source_sentence: Light-chain amyloidosis
+  sentences:
+  - partial deletion of the long arm of chromosome X
+  - Teneria teneriensis
+  - amyloidosis primary systemic
+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: owl ontology eval
+      type: owl_ontology_eval
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6302799165287473
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8147801683816651
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8775275239260272
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9268187378570915
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6302799165287473
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27634261591230724
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17979420018709072
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09566812981218968
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6216929313281044
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8081120625554675
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8723585426111152
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9241442997289582
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7796907170635903
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7342337217921898
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.734065731352359
+      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:** 1024 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': 1024, '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/bond-embed-16L")
+# Run inference
+sentences = [
+    'Light-chain amyloidosis',
+    'amyloidosis primary systemic',
+    'partial deletion of the long arm of chromosome X',
+]
+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: `owl_ontology_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   | 0.6303     |
+| cosine_accuracy@3   | 0.8148     |
+| cosine_accuracy@5   | 0.8775     |
+| cosine_accuracy@10  | 0.9268     |
+| cosine_precision@1  | 0.6303     |
+| cosine_precision@3  | 0.2763     |
+| cosine_precision@5  | 0.1798     |
+| cosine_precision@10 | 0.0957     |
+| cosine_recall@1     | 0.6217     |
+| cosine_recall@3     | 0.8081     |
+| cosine_recall@5     | 0.8724     |
+| cosine_recall@10    | 0.9241     |
+| **cosine_ndcg@10**  | **0.7797** |
+| cosine_mrr@10       | 0.7342     |
+| cosine_map@100      | 0.7341     |
+
+<!--
+## 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: 1,441,905 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                         |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 9.48 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.68 tokens</li><li>max: 30 tokens</li></ul> |
+* Samples:
+  | anchor                               | positive                             |
+  |:-------------------------------------|:-------------------------------------|
+  | <code>Mangshan horned toad</code>    | <code>Mangshan spadefoot toad</code> |
+  | <code>Leuconotopicos borealis</code> | <code>Picoides borealis</code>       |
+  | <code>Cylindrella teneriensis</code> | <code>Teneria teneriensis</code>     |
+* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 1024
+- `learning_rate`: 1.5e-05
+- `num_train_epochs`: 5
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.05
+- `bf16`: True
+- `dataloader_num_workers`: 32
+- `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`: 1024
+- `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`: 1.5e-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`: 5
+- `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`: 32
+- `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 | owl_ontology_eval_cosine_ndcg@10 |
+|:------:|:----:|:-------------:|:--------------------------------:|
+| 0.0717 | 100  | 1.3232        | -                                |
+| 0.1434 | 200  | 1.021         | -                                |
+| 0.2151 | 300  | 0.9633        | -                                |
+| 0.2867 | 400  | 0.9068        | -                                |
+| 0.3297 | 460  | -             | 0.7207                           |
+| 0.3584 | 500  | 0.8723        | -                                |
+| 0.4301 | 600  | 0.852         | -                                |
+| 0.5018 | 700  | 0.8161        | -                                |
+| 0.5735 | 800  | 0.7939        | -                                |
+| 0.6452 | 900  | 0.7935        | -                                |
+| 0.6595 | 920  | -             | 0.7364                           |
+| 0.7168 | 1000 | 0.7646        | -                                |
+| 0.7885 | 1100 | 0.7464        | -                                |
+| 0.8602 | 1200 | 0.7376        | -                                |
+| 0.9319 | 1300 | 0.7313        | -                                |
+| 0.9892 | 1380 | -             | 0.7468                           |
+| 1.0036 | 1400 | 0.7099        | -                                |
+| 1.0753 | 1500 | 0.6884        | -                                |
+| 1.1470 | 1600 | 0.6776        | -                                |
+| 1.2186 | 1700 | 0.6694        | -                                |
+| 1.2903 | 1800 | 0.6641        | -                                |
+| 1.3190 | 1840 | -             | 0.7561                           |
+| 1.3620 | 1900 | 0.6526        | -                                |
+| 1.4337 | 2000 | 0.6524        | -                                |
+| 1.5054 | 2100 | 0.6364        | -                                |
+| 1.5771 | 2200 | 0.6339        | -                                |
+| 1.6487 | 2300 | 0.626         | 0.7614                           |
+| 1.7204 | 2400 | 0.6197        | -                                |
+| 1.7921 | 2500 | 0.6193        | -                                |
+| 1.8638 | 2600 | 0.6155        | -                                |
+| 1.9355 | 2700 | 0.6142        | -                                |
+| 1.9785 | 2760 | -             | 0.7662                           |
+| 2.0072 | 2800 | 0.5853        | -                                |
+| 2.0789 | 2900 | 0.5824        | -                                |
+| 2.1505 | 3000 | 0.5769        | -                                |
+| 2.2222 | 3100 | 0.5765        | -                                |
+| 2.2939 | 3200 | 0.5608        | -                                |
+| 2.3082 | 3220 | -             | 0.7698                           |
+| 2.3656 | 3300 | 0.5695        | -                                |
+| 2.4373 | 3400 | 0.5641        | -                                |
+| 2.5090 | 3500 | 0.5638        | -                                |
+| 2.5806 | 3600 | 0.554         | -                                |
+| 2.6380 | 3680 | -             | 0.7735                           |
+| 2.6523 | 3700 | 0.5539        | -                                |
+| 2.7240 | 3800 | 0.5495        | -                                |
+| 2.7957 | 3900 | 0.5556        | -                                |
+| 2.8674 | 4000 | 0.5397        | -                                |
+| 2.9391 | 4100 | 0.5447        | -                                |
+| 2.9677 | 4140 | -             | 0.7757                           |
+| 3.0108 | 4200 | 0.5331        | -                                |
+| 3.0824 | 4300 | 0.5336        | -                                |
+| 3.1541 | 4400 | 0.5346        | -                                |
+| 3.2258 | 4500 | 0.5247        | -                                |
+| 3.2975 | 4600 | 0.5241        | 0.7775                           |
+| 3.3692 | 4700 | 0.5257        | -                                |
+| 3.4409 | 4800 | 0.5241        | -                                |
+| 3.5125 | 4900 | 0.5171        | -                                |
+| 3.5842 | 5000 | 0.5215        | -                                |
+| 3.6272 | 5060 | -             | 0.7787                           |
+| 3.6559 | 5100 | 0.5203        | -                                |
+| 3.7276 | 5200 | 0.5214        | -                                |
+| 3.7993 | 5300 | 0.5266        | -                                |
+| 3.8710 | 5400 | 0.5127        | -                                |
+| 3.9427 | 5500 | 0.5062        | -                                |
+| 3.9570 | 5520 | -             | 0.7790                           |
+| 4.0143 | 5600 | 0.5104        | -                                |
+| 4.0860 | 5700 | 0.5155        | -                                |
+| 4.1577 | 5800 | 0.5042        | -                                |
+| 4.2294 | 5900 | 0.5174        | -                                |
+| 4.2867 | 5980 | -             | 0.7797                           |
+| 4.3011 | 6000 | 0.509         | -                                |
+| 4.3728 | 6100 | 0.5106        | -                                |
+| 4.4444 | 6200 | 0.5076        | -                                |
+| 4.5161 | 6300 | 0.5046        | -                                |
+| 4.5878 | 6400 | 0.5077        | -                                |
+| 4.6165 | 6440 | -             | 0.7795                           |
+| 4.6595 | 6500 | 0.5114        | -                                |
+| 4.7312 | 6600 | 0.5103        | -                                |
+| 4.8029 | 6700 | 0.5106        | -                                |
+| 4.8746 | 6800 | 0.5102        | -                                |
+| 4.9462 | 6900 | 0.5076        | 0.7797                           |
+
+
+### 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",
+}
+```
+
+#### CachedMultipleNegativesRankingLoss
+```bibtex
+@misc{gao2021scaling,
+    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
+    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
+    year={2021},
+    eprint={2101.06983},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## 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": "float16",
+  "transformers_version": "4.55.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32768
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.55.1",
+    "pytorch": "2.6.0+cu124"
+  },
+  "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:d3a29b7add13de3dc74ca7dd1f7cbcad70b9b9d8ddb1cf01410e15242cf9aa84
+size 83062992

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": 1024,
+    "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": {
+      "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": false,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 1024,
+  "model_max_length": 1024,
+  "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]"
+}