Upload model from ../experiments/HiT-biobert-v1.1-icd9-temp/final

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
+- dense
+- generated_from_trainer
+- dataset_size:148295
+- loss:SymmetricLoss
+base_model: dmis-lab/biobert-v1.1
+widget:
+- source_sentence: Complications of pregnancy; childbirth; and the puerperium → Complications
+    during labor → Forceps delivery
+  sentences:
+  - Complications of pregnancy; childbirth; and the puerperium → Complications during
+    labor
+  - Complications of pregnancy; childbirth; and the puerperium → Other complications
+    of birth; puerperium affecting management of mother
+  - Complications of pregnancy; childbirth; and the puerperium → Normal pregnancy
+    and/or delivery → Other pregnancy and delivery including normal
+- source_sentence: Complications of pregnancy; childbirth; and the puerperium → Complications
+    mainly related to pregnancy → Early or threatened labor
+  sentences:
+  - Complications of pregnancy; childbirth; and the puerperium → Complications mainly
+    related to pregnancy
+  - Complications of pregnancy; childbirth; and the puerperium → Abortion-related
+    disorders → Postabortion complications
+  - Complications of pregnancy; childbirth; and the puerperium → Indications for care
+    in pregnancy; labor; and delivery
+- source_sentence: Diseases of the respiratory system → Respiratory infections → Acute
+    bronchitis
+  sentences:
+  - Diseases of the respiratory system → Asthma → Asthma
+  - Diseases of the respiratory system → Lung disease due to external agents
+  - Diseases of the respiratory system → Respiratory infections
+- source_sentence: Diseases of the circulatory system → Diseases of the heart → Cardiac
+    arrest and ventricular fibrillation
+  sentences:
+  - Diseases of the circulatory system → Hypertension → Essential hypertension
+  - Diseases of the circulatory system → Cerebrovascular disease
+  - Diseases of the circulatory system → Diseases of the heart
+- source_sentence: Infectious and parasitic diseases → Mycoses
+  sentences:
+  - Diseases of the skin and subcutaneous tissue → Skin and subcutaneous tissue infections
+  - Mental illness
+  - Infectious and parasitic diseases
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# HierarchyTransformer based on dmis-lab/biobert-v1.1
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1). 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:** [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) <!-- at revision 551ca18efd7f052c8dfa0b01c94c2a8e68bc5488 -->
+- **Maximum Sequence Length:** 256 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/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+HierarchyTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': '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 = [
+    'Infectious and parasitic diseases → Mycoses',
+    'Infectious and parasitic diseases',
+    'Mental illness',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.6610, 0.3361],
+#         [0.6610, 1.0000, 0.2730],
+#         [0.3361, 0.2730, 1.0000]])
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## 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: 148,295 training samples
+* Columns: <code>child</code>, <code>parent</code>, <code>parent_negative</code>, and <code>child_negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | child                                                                             | parent                                                                            | parent_negative                                                                   | child_negative                                                                     |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 25.19 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.22 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.94 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 23.48 tokens</li><li>max: 65 tokens</li></ul> |
+* Samples:
+  | child                                                                | parent                                         | parent_negative             | child_negative                                                                                      |
+  |:---------------------------------------------------------------------|:-----------------------------------------------|:----------------------------|:----------------------------------------------------------------------------------------------------|
+  | <code>Infectious and parasitic diseases → Bacterial infection</code> | <code>Infectious and parasitic diseases</code> | <code>Mental illness</code> | <code>Diseases of the nervous system and sense organs → Central nervous system infection</code>     |
+  | <code>Infectious and parasitic diseases → Bacterial infection</code> | <code>Infectious and parasitic diseases</code> | <code>Mental illness</code> | <code>Diseases of the digestive system → Intestinal infection</code>                                |
+  | <code>Infectious and parasitic diseases → Bacterial infection</code> | <code>Infectious and parasitic diseases</code> | <code>Mental illness</code> | <code>Diseases of the skin and subcutaneous tissue → Skin and subcutaneous tissue infections</code> |
+* Loss: <code>hierarchy_transformers.losses.symmetric_loss.SymmetricLoss</code> with these parameters:
+  ```json
+  {
+      "distance_metric": "PoincareBall(c=0.0013021096820011735).dist and dist0",
+      "HyperbolicChildTriplet": {
+          "weight": 1.0,
+          "distance_metric": "PoincareBall(c=0.0013021096820011735).dist",
+          "margin": 3.0
+      },
+      "HyperbolicParentTriplet": {
+          "weight": 1.0,
+          "distance_metric": "PoincareBall(c=0.0013021096820011735).dist",
+          "margin": 3.0
+      }
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 512
+- `learning_rate`: 1e-05
+- `num_train_epochs`: 10
+- `warmup_steps`: 500
+- `load_best_model_at_end`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 512
+- `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`: 1e-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`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 500
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `bf16`: False
+- `fp16`: 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`: 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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: no
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch   | Step     | Training Loss |
+|:-------:|:--------:|:-------------:|
+| 0.0863  | 100      | 2.1613        |
+| 0.1726  | 200      | 0.5936        |
+| 0.2588  | 300      | 0.1998        |
+| 0.3451  | 400      | 0.1107        |
+| 0.4314  | 500      | 0.0567        |
+| 0.5177  | 600      | 0.0452        |
+| 0.6040  | 700      | 0.032         |
+| 0.6903  | 800      | 0.0279        |
+| 0.7765  | 900      | 0.0218        |
+| 0.8628  | 1000     | 0.0235        |
+| 0.9491  | 1100     | 0.018         |
+| 1.0     | 1159     | -             |
+| 1.0354  | 1200     | 0.0192        |
+| 1.1217  | 1300     | 0.0176        |
+| 1.2079  | 1400     | 0.0137        |
+| 1.2942  | 1500     | 0.0119        |
+| 1.3805  | 1600     | 0.0139        |
+| 1.4668  | 1700     | 0.0138        |
+| 1.5531  | 1800     | 0.0123        |
+| 1.6393  | 1900     | 0.0104        |
+| 1.7256  | 2000     | 0.0117        |
+| 1.8119  | 2100     | 0.0097        |
+| 1.8982  | 2200     | 0.0133        |
+| 1.9845  | 2300     | 0.01          |
+| **2.0** | **2318** | **-**         |
+| 2.0708  | 2400     | 0.0109        |
+| 2.1570  | 2500     | 0.0074        |
+| 2.2433  | 2600     | 0.0072        |
+| 2.3296  | 2700     | 0.015         |
+| 2.4159  | 2800     | 0.0069        |
+| 2.5022  | 2900     | 0.0107        |
+| 2.5884  | 3000     | 0.0094        |
+| 2.6747  | 3100     | 0.0105        |
+| 2.7610  | 3200     | 0.0095        |
+| 2.8473  | 3300     | 0.0072        |
+| 2.9336  | 3400     | 0.0084        |
+| 3.0     | 3477     | -             |
+| 3.0198  | 3500     | 0.0104        |
+| 3.1061  | 3600     | 0.0078        |
+| 3.1924  | 3700     | 0.008         |
+| 3.2787  | 3800     | 0.0086        |
+| 3.3650  | 3900     | 0.0085        |
+| 3.4513  | 4000     | 0.0081        |
+| 3.5375  | 4100     | 0.0093        |
+| 3.6238  | 4200     | 0.0107        |
+| 3.7101  | 4300     | 0.008         |
+| 3.7964  | 4400     | 0.0099        |
+| 3.8827  | 4500     | 0.0058        |
+| 3.9689  | 4600     | 0.0084        |
+| 4.0     | 4636     | -             |
+| 4.0552  | 4700     | 0.01          |
+| 4.1415  | 4800     | 0.0053        |
+| 4.2278  | 4900     | 0.0075        |
+| 4.3141  | 5000     | 0.0077        |
+| 4.4003  | 5100     | 0.0065        |
+| 4.4866  | 5200     | 0.0089        |
+| 4.5729  | 5300     | 0.0082        |
+| 4.6592  | 5400     | 0.0093        |
+| 4.7455  | 5500     | 0.0076        |
+| 4.8318  | 5600     | 0.0095        |
+| 4.9180  | 5700     | 0.0078        |
+| 5.0     | 5795     | -             |
+| 5.0043  | 5800     | 0.0055        |
+| 5.0906  | 5900     | 0.0061        |
+| 5.1769  | 6000     | 0.005         |
+| 5.2632  | 6100     | 0.0075        |
+| 5.3494  | 6200     | 0.0079        |
+| 5.4357  | 6300     | 0.006         |
+| 5.5220  | 6400     | 0.0095        |
+| 5.6083  | 6500     | 0.0099        |
+| 5.6946  | 6600     | 0.0084        |
+| 5.7808  | 6700     | 0.008         |
+| 5.8671  | 6800     | 0.0064        |
+| 5.9534  | 6900     | 0.0097        |
+| 6.0     | 6954     | -             |
+| 6.0397  | 7000     | 0.0063        |
+| 6.1260  | 7100     | 0.0069        |
+| 6.2123  | 7200     | 0.0095        |
+| 6.2985  | 7300     | 0.0067        |
+| 6.3848  | 7400     | 0.0056        |
+| 6.4711  | 7500     | 0.0074        |
+| 6.5574  | 7600     | 0.0086        |
+| 6.6437  | 7700     | 0.0072        |
+| 6.7299  | 7800     | 0.0065        |
+| 6.8162  | 7900     | 0.0052        |
+| 6.9025  | 8000     | 0.0101        |
+| 6.9888  | 8100     | 0.0086        |
+| 7.0     | 8113     | -             |
+| 7.0751  | 8200     | 0.0065        |
+| 7.1613  | 8300     | 0.0106        |
+| 7.2476  | 8400     | 0.0049        |
+| 7.3339  | 8500     | 0.0074        |
+| 7.4202  | 8600     | 0.0065        |
+| 7.5065  | 8700     | 0.004         |
+| 7.5928  | 8800     | 0.0075        |
+| 7.6790  | 8900     | 0.009         |
+| 7.7653  | 9000     | 0.0059        |
+| 7.8516  | 9100     | 0.0063        |
+| 7.9379  | 9200     | 0.0095        |
+| 8.0     | 9272     | -             |
+| 8.0242  | 9300     | 0.0082        |
+| 8.1104  | 9400     | 0.0067        |
+| 8.1967  | 9500     | 0.0063        |
+| 8.2830  | 9600     | 0.0071        |
+| 8.3693  | 9700     | 0.0064        |
+| 8.4556  | 9800     | 0.0072        |
+| 8.5418  | 9900     | 0.0059        |
+| 8.6281  | 10000    | 0.0085        |
+| 8.7144  | 10100    | 0.0083        |
+| 8.8007  | 10200    | 0.0046        |
+| 8.8870  | 10300    | 0.0055        |
+| 8.9733  | 10400    | 0.008         |
+| 9.0     | 10431    | -             |
+| 9.0595  | 10500    | 0.0066        |
+| 9.1458  | 10600    | 0.0068        |
+| 9.2321  | 10700    | 0.0093        |
+| 9.3184  | 10800    | 0.0067        |
+| 9.4047  | 10900    | 0.0054        |
+| 9.4909  | 11000    | 0.0079        |
+| 9.5772  | 11100    | 0.0052        |
+| 9.6635  | 11200    | 0.0073        |
+| 9.7498  | 11300    | 0.0088        |
+| 9.8361  | 11400    | 0.005         |
+| 9.9223  | 11500    | 0.0069        |
+| 10.0    | 11590    | -             |
+
+* The bold row denotes the saved checkpoint.
+</details>
+
+### Framework Versions
+- Python: 3.10.13
+- Sentence Transformers: 5.1.2
+- Transformers: 4.57.1
+- PyTorch: 2.9.0+cu128
+- Accelerate: 1.11.0
+- Datasets: 4.3.0
+- Tokenizers: 0.22.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### SymmetricLoss
+```bibtex
+@article{he2024language,
+  title={Language models as hierarchy encoders},
+  author={He, Yuan and Yuan, Zhangdie and Chen, Jiaoyan and Horrocks, Ian},
+  journal={arXiv preprint arXiv:2401.11374},
+  year={2024}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 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",
+  "transformers_version": "4.57.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "HierarchyTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.57.1",
+    "pytorch": "2.9.0+cu128"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9e98e0837b48f23cbfb96e8d7534a0c2a80a3fffcc27819b8cfc9ee0b890b83
+size 433263448

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 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

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": false,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}