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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+---
+language: []
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:5019
+- loss:TripletLoss
+base_model: Davlan/bert-base-multilingual-cased-finetuned-yoruba
+datasets: []
+metrics:
+- cosine_accuracy
+- dot_accuracy
+- manhattan_accuracy
+- euclidean_accuracy
+- max_accuracy
+widget:
+- source_sentence: Bawo ni eniyan lasan ṣe le ṣe agbaye ni aye ti o dara julọ?
+  sentences:
+  - Ewo ni fiimu ti o dara julọ ti agbaye?
+  - Bawo ni a ṣe le ṣe agbaye ni aye ti o dara julọ fun gbogbo ati fun iran iwaju
+    lati wa?
+  - Njẹ aiye yii dara julọ tabi buru?
+- source_sentence: Ni Pokemon ati tẹmpili ti okun, kilode ti o yanilenu Manicy?
+  sentences:
+  - Kini idi ti Manafy ọmọ-ọwọ ni Pokémon ger ati tẹmpili ti okun?
+  - Bawo ni awọn ibeere mi ṣe wa nigbagbogbo nigbagbogbo lori Quora?
+  - Ṣe "Pokémon ti o wuyi ati tẹmpili ti Okun" ka akọku?
+- source_sentence: Kini itumo igbesi aye yii?
+  sentences:
+  - Kini "Gbe igbesi aye rẹ" tumọ si?
+  - Kini o ro pe o jẹ itumọ ti igbesi aye?
+  - Nitorinaa bawo ni MO ṣe le gba meth lati fulu jade ninu ara ni awọn wakati 2 ṣaaju
+    idanwo togbo kan?
+- source_sentence: Nibo ni MO le gba ọpọlọpọ awọn aso deede, awọn aṣọ alekun & awọn
+    aṣọ irọlẹ ni goolu ni eti okun?
+  sentences:
+  - Nibo ni MO le gba ọpọlọpọ awọn awọ ati titobi fun awọn aṣọ awọn alagbaje ni Gold
+    Coast?
+  - Kini yoo ṣẹlẹ ti o ba jẹ ki o dina nkan bi Facebook tabi Google ni isansa ti iṣan
+    neta?
+  - Nibo ni MO le gba ikojọpọ ti o lẹwa fun awọn aṣọ igbeyawo ni Sydney?
+- source_sentence: Kini o yẹ ki Ilu India ṣe lori ikọlu UI?
+  sentences:
+  - Bawo ni MO ṣe sọ Gẹẹsi leta ni ifọrọwanilẹnuwo kan?
+  - Lẹhin gbogbo họọsi ti media media ti ṣẹda awọn ikọlu URI Wip, kii yoo jẹ ohun
+    itiju fun India ti ko ba kọlu Pakistan?
+  - Bawo ni India le dahun si ikọlu ẹru UI?
+pipeline_tag: sentence-similarity
+model-index:
+- name: SentenceTransformer based on Davlan/bert-base-multilingual-cased-finetuned-yoruba
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy
+      value: 0.865
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.135
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.868
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.868
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.868
+      name: Max Accuracy
+---
+
+# SentenceTransformer based on Davlan/bert-base-multilingual-cased-finetuned-yoruba
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Davlan/bert-base-multilingual-cased-finetuned-yoruba](https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-yoruba). 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:** [Davlan/bert-base-multilingual-cased-finetuned-yoruba](https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-yoruba) <!-- at revision 000f80b4509f73bca9a33f9db0573d6f67396a12 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **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("adejumobi/bert-base-multilingual-cased-finetuned-yoruba-IR")
+# Run inference
+sentences = [
+    'Kini o yẹ ki Ilu India ṣe lori ikọlu UI?',
+    'Bawo ni India le dahun si ikọlu ẹru UI?',
+    'Lẹhin gbogbo họọsi ti media media ti ṣẹda awọn ikọlu URI Wip, kii yoo jẹ ohun itiju fun India ti ko ba kọlu Pakistan?',
+]
+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
+
+#### Triplet
+
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| **cosine_accuracy** | **0.865** |
+| dot_accuracy        | 0.135     |
+| manhattan_accuracy  | 0.868     |
+| euclidean_accuracy  | 0.868     |
+| max_accuracy        | 0.868     |
+
+<!--
+## 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: 5,019 training samples
+* Columns: <code>query</code>, <code>pos</code>, and <code>neg</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                             | pos                                                                               | neg                                                                               |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 24.62 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 24.14 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.71 tokens</li><li>max: 98 tokens</li></ul> |
+* Samples:
+  | query                                                                                      | pos                                                                                               | neg                                                                                                                 |
+  |:-------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|
+  | <code>Kini idi ti Ilu India ṣe a ko ni ọkan lori ijiroro oloselu kan bi ni AMẸRIKA?</code> | <code>Kini idi ti a ko le ni ijiroro gbangba laarin awọn oloselu ni India bi ọkan ninu wa?</code> | <code>Njẹ eniyan le da quo duro de India Pakistan ariyanjiyan?A ni aisan ati ti o ri eyi lojoojumọ ni olopo?</code> |
+  | <code>Kini OnePlus Ọkan?</code>                                                            | <code>Bawo ni OnePlus kan?</code>                                                                 | <code>Kini idi ti OnePlus Ọkan dara?</code>                                                                         |
+  | <code>Ṣe ọkan wa ṣe iṣakoso awọn ẹdun wa?</code>                                           | <code>Bawo ni ọlọgbọn ati awọn eniyan aṣeyọri ṣe ṣakoso awọn ẹdun wọn?</code>                     | <code>Bawo ni MO ṣe le ṣakoso awọn ẹdun mi rere fun awọn eniyan ti Mo nifẹ ṣugbọn wọn ko bikita nipa mi?</code>     |
+* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
+      "triplet_margin": 5
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 1,000 evaluation samples
+* Columns: <code>query</code>, <code>pos</code>, and <code>neg</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                             | pos                                                                                | neg                                                                                |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 24.32 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 24.06 tokens</li><li>max: 115 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 25.58 tokens</li><li>max: 121 tokens</li></ul> |
+* Samples:
+  | query                                                                            | pos                                                                                          | neg                                                                                             |
+  |:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
+  | <code>Bawo ni o jẹ ọjọ ebi?</code>                                               | <code>Bawo ni o jẹ ọsan</code>                                                               | <code>Njẹ NEBM lueMo ṣẹlẹ lati wa awọn ifiweranṣẹ ti o sọ pe o jẹ iro ati pe ko ni itter</code> |
+  | <code>Kini awọn ohun elo akọkọ ti kọnputa kan?</code>                            | <code>Kini diẹ ninu awọn ẹya akọkọ ti kọnputa kan?Awọn iṣẹ wo ni wọn nṣe iranṣẹ?</code>      | <code>Kini awọn eto kọmputa?Kini awọn iṣẹ ti awọn eto kọnputa?</code>                           |
+  | <code>Ṣe o le faffiti Artists fun sokiri Graffiti ni Rockdale County, GA?</code> | <code>Ṣe o le fun awọn ojukokoro fun fun sokiri Graffiti ni Cockdale County, Georgia?</code> | <code>Kini idi ti Graffiti jẹ arufin?</code>                                                    |
+* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
+      "triplet_margin": 5
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 12
+- `per_device_eval_batch_size`: 3
+- `learning_rate`: 1e-05
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### 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`: 12
+- `per_device_eval_batch_size`: 3
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_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`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `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`: 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}
+- `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
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | loss   | cosine_accuracy |
+|:------:|:----:|:-------------:|:------:|:---------------:|
+| 0      | 0    | -             | -      | 0.827           |
+| 0.2387 | 100  | 4.247         | 3.6056 | 0.815           |
+| 0.4773 | 200  | 3.3576        | 2.7548 | 0.809           |
+| 0.7160 | 300  | 2.931         | 2.3805 | 0.843           |
+| 0.9547 | 400  | 2.4476        | 2.1895 | 0.858           |
+| 1.1933 | 500  | 2.5839        | 2.1148 | 0.854           |
+| 1.4320 | 600  | 2.0645        | 2.0497 | 0.855           |
+| 1.6706 | 700  | 1.8386        | 2.0328 | 0.847           |
+| 1.9093 | 800  | 1.5527        | 1.9380 | 0.857           |
+| 2.1480 | 900  | 1.7298        | 1.8999 | 0.861           |
+| 2.3866 | 1000 | 1.4375        | 1.8744 | 0.855           |
+| 2.6253 | 1100 | 1.1605        | 1.8761 | 0.861           |
+| 2.8640 | 1200 | 1.0601        | 1.8658 | 0.862           |
+| 3.1026 | 1300 | 1.1019        | 1.8181 | 0.861           |
+| 3.3413 | 1400 | 1.052         | 1.8088 | 0.854           |
+| 3.5800 | 1500 | 0.8807        | 1.7937 | 0.862           |
+| 3.8186 | 1600 | 0.7877        | 1.7963 | 0.862           |
+| 4.0573 | 1700 | 0.7613        | 1.7869 | 0.868           |
+| 4.2959 | 1800 | 0.8018        | 1.7696 | 0.867           |
+| 4.5346 | 1900 | 0.6717        | 1.7815 | 0.865           |
+| 4.7733 | 2000 | 0.6603        | 1.7776 | 0.865           |
+
+
+### Framework Versions
+- Python: 3.10.13
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.1.2
+- Accelerate: 0.31.0
+- Datasets: 2.19.2
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+#### TripletLoss
+```bibtex
+@misc{hermans2017defense,
+    title={In Defense of the Triplet Loss for Person Re-Identification}, 
+    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
+    year={2017},
+    eprint={1703.07737},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+<!--
+## 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 +1,32 @@
+{
+  "_name_or_path": "Davlan/bert-base-multilingual-cased-finetuned-yoruba",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "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,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 119547
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.1.2"
+  },
+  "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:b10709d4986b096d316a7b1f87278239c5034611c0a007dc5bc32e6d3b9ae5ff
+size 711436136

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

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
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+      "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,
+  "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]"
+}