Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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: BAAI/bge-large-en
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+- dot_accuracy
+- manhattan_accuracy
+- euclidean_accuracy
+- max_accuracy
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:22604
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: 23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC
+    Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations
+    - QC Lab
+  sentences:
+  - 'mat-3783s5 : 3783 Seq 5 - Material Order'
+  - '21-1313-2.0 : Layout Drawings'
+  - '26-0500-1.0a : Breakers (2P 20A)'
+- source_sentence: 23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC
+    Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations
+    - QC Lab
+  sentences:
+  - '26-0500-1.3 : Cabling / Wiring'
+  - '26-0500-1.0a : Breakers (2P 20A)'
+  - '23-2000-1.1 : HWR and HWS Pipe, Valves and Fittings'
+- source_sentence: 3783 UC Davis (Northern Cal - Jon Sanguinetti)->Seq 5-P-3783
+  sentences:
+  - 'mat-3783s8 : 3783 Seq 8 - Material Order'
+  - 'mat-3783s5 : 3783 Seq 5 - Material Order'
+  - 'mat-3786s18 : 3786 Seq 18 - Material Order'
+- source_sentence: 3786 Rady (Pacific - JD Hudson)->Seq 18-P-3786
+  sentences:
+  - '26-0500-1.0a : Breakers (2P 20A)'
+  - 'dwg-3786s18 : 3786 Seq 18 - Drawings'
+  - '23-7000-4.0b : EAV-91623'
+- source_sentence: 3783 UC Davis (Northern Cal - Jon Sanguinetti)->Seq 18-P-3783
+  sentences:
+  - 'mat-3783s5 : 3783 Seq 5 - Material Order'
+  - 'dwg-3783s8 : 3783 Seq 8 - Drawings'
+  - 'dwg-3783s18 : 3783 Seq 18 - Drawings'
+model-index:
+- name: SentenceTransformer based on BAAI/bge-large-en
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: custom bge dev
+      type: custom-bge-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9838187702265372
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.016181229773462782
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.9838187702265372
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.9838187702265372
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.9838187702265372
+      name: Max Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: custom bge test
+      type: custom-bge-test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9838187702265372
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.016181229773462782
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.9838187702265372
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.9838187702265372
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.9838187702265372
+      name: Max Accuracy
+---
+
+# SentenceTransformer based on BAAI/bge-large-en
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) <!-- at revision abe7d9d814b775ca171121fb03f394dc42974275 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 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': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## 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("rnbokade/custom-bge")
+# Run inference
+sentences = [
+    '3783 UC Davis (Northern Cal - Jon Sanguinetti)->Seq 18-P-3783',
+    'dwg-3783s18 : 3783 Seq 18 - Drawings',
+    'mat-3783s5 : 3783 Seq 5 - Material Order',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# 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
+* Dataset: `custom-bge-dev`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| cosine_accuracy    | 0.9838     |
+| dot_accuracy       | 0.0162     |
+| manhattan_accuracy | 0.9838     |
+| euclidean_accuracy | 0.9838     |
+| **max_accuracy**   | **0.9838** |
+
+#### Triplet
+* Dataset: `custom-bge-test`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| cosine_accuracy    | 0.9838     |
+| dot_accuracy       | 0.0162     |
+| manhattan_accuracy | 0.9838     |
+| euclidean_accuracy | 0.9838     |
+| **max_accuracy**   | **0.9838** |
+
+<!--
+## 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: 22,604 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: 22 tokens</li><li>mean: 25.35 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 18.84 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.74 tokens</li><li>max: 38 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                         | positive                                                 | negative                                                |
+  |:-------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------|:--------------------------------------------------------|
+  | <code>MOD 1- Metal Decking - Floor<br>Stud Wall Panels<br>Floor Sheathing (Megaboard) Layout of Dirtt Frame Centerlines</code> | <code>EW1001-125 : Door Slabs / Frames / Hardware</code> | <code>dwg-3783s16 : 3783 Seq 16 - Drawings</code>       |
+  | <code>MOD 1- Metal Decking - Floor<br>Stud Wall Panels<br>Floor Sheathing (Megaboard) Layout of Dirtt Frame Centerlines</code> | <code>EW1001-125 : Door Slabs / Frames / Hardware</code> | <code>mat-3783s16 : 3783 Seq 16 - Material Order</code> |
+  | <code>MOD 1- Metal Decking - Floor<br>Stud Wall Panels<br>Floor Sheathing (Megaboard) Layout of Dirtt Frame Centerlines</code> | <code>EW1001-125 : Door Slabs / Frames / Hardware</code> | <code>dwg-3786s292 : 3786 Seq 292 - Drawings</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"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 618 evaluation 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: 22 tokens</li><li>mean: 33.18 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 17.48 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 17.48 tokens</li><li>max: 22 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                | positive                                     | negative                                                |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------|:--------------------------------------------------------|
+  | <code>23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab</code> | <code>26-0500-1.0 : Breakers (3P 20A)</code> | <code>dwg-3786s17 : 3786 Seq 17 - Drawings</code>       |
+  | <code>23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab</code> | <code>26-0500-1.0 : Breakers (3P 20A)</code> | <code>mat-3786s17 : 3786 Seq 17 - Material Order</code> |
+  | <code>23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab</code> | <code>26-0500-1.0 : Breakers (3P 20A)</code> | <code>09-9000-2.0 : Paint and Coatings</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
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 1
+- `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`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `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
+- `eval_on_start`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | loss   | custom-bge-dev_max_accuracy | custom-bge-test_max_accuracy |
+|:------:|:----:|:-------------:|:------:|:---------------------------:|:----------------------------:|
+| 0      | 0    | -             | -      | 0.8463                      | -                            |
+| 0.0708 | 100  | 0.5651        | 0.6065 | 0.9919                      | -                            |
+| 0.1415 | 200  | 0.168         | 0.4217 | 0.9935                      | -                            |
+| 0.2123 | 300  | 0.0499        | 0.6747 | 0.9951                      | -                            |
+| 0.2831 | 400  | 0.2205        | 0.8112 | 0.9951                      | -                            |
+| 0.3539 | 500  | 0.1167        | 0.7040 | 0.9903                      | -                            |
+| 0.4246 | 600  | 0.0968        | 0.7364 | 0.9822                      | -                            |
+| 0.4954 | 700  | 0.1704        | 0.5540 | 0.9968                      | -                            |
+| 0.5662 | 800  | 0.1104        | 0.7266 | 0.9951                      | -                            |
+| 0.6369 | 900  | 0.1698        | 1.1020 | 0.9725                      | -                            |
+| 0.7077 | 1000 | 0.1077        | 0.9028 | 0.9790                      | -                            |
+| 0.7785 | 1100 | 0.1667        | 0.8478 | 0.9757                      | -                            |
+| 0.8493 | 1200 | 0.0707        | 0.7629 | 0.9887                      | -                            |
+| 0.9200 | 1300 | 0.0299        | 0.8024 | 0.9871                      | -                            |
+| 0.9908 | 1400 | 0.0005        | 0.8161 | 0.9838                      | -                            |
+| 1.0    | 1413 | -             | -      | -                           | 0.9838                       |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.4
+- PyTorch: 2.3.1+cu121
+- Accelerate: 0.32.1
+- Datasets: 2.21.0
+- 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",
+}
+```
+
+#### 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 +1,32 @@
+{
+  "_name_or_path": "BAAI/bge-large-en",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.42.4",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:884cbb86afb3d05b5aea3a775a87d338ebac2f8f33a8b90f355e6023386c0447
+size 1340612432

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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

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
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "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]"
+}