Add new SentenceTransformer model.

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+language: []
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dataset_size:10K<n<100K
+- loss:MultipleNegativesSymmetricRankingLoss
+base_model: distilbert/distilbert-base-uncased
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+widget:
+- source_sentence: what is GOGO
+  sentences:
+  - What is Viasat
+  - are we flying into Tel Aviv
+  - how do i correct a name in term
+- source_sentence: What is EU 261
+  sentences:
+  - is puj a EU compensation country
+  - can i take my bicycle on af
+  - flight delays over 6 hours
+- source_sentence: Can i get wifi
+  sentences:
+  - which aircrafts do not have wifi
+  - military traveling with pet
+  - baggage delay to carousel
+- source_sentence: austin airport
+  sentences:
+  - What time is IAH open
+  - amex card free checked bag
+  - what is upgrade companion
+- source_sentence: pets in cargo
+  sentences:
+  - can a pet travel in cargo
+  - baggage exceptions for Amex
+  - how do I get sky priority
+pipeline_tag: sentence-similarity
+model-index:
+- name: SentenceTransformer based on distilbert/distilbert-base-uncased
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: eval examples
+      type: eval_examples
+    metrics:
+    - type: pearson_cosine
+      value: .nan
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: .nan
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: .nan
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: .nan
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: .nan
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: .nan
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: .nan
+      name: Pearson Dot
+    - type: spearman_dot
+      value: .nan
+      name: Spearman Dot
+    - type: pearson_max
+      value: .nan
+      name: Pearson Max
+    - type: spearman_max
+      value: .nan
+      name: Spearman Max
+---
+
+# SentenceTransformer based on distilbert/distilbert-base-uncased
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased). 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:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
+- **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: DistilBertModel 
+  (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("pjbhaumik/biencoder-finetune-model-v9")
+# Run inference
+sentences = [
+    'pets in cargo',
+    'can a pet travel in cargo',
+    'baggage exceptions for Amex',
+]
+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
+
+#### Semantic Similarity
+* Dataset: `eval_examples`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric             | Value   |
+|:-------------------|:--------|
+| pearson_cosine     | nan     |
+| spearman_cosine    | nan     |
+| pearson_manhattan  | nan     |
+| spearman_manhattan | nan     |
+| pearson_euclidean  | nan     |
+| spearman_euclidean | nan     |
+| pearson_dot        | nan     |
+| spearman_dot       | nan     |
+| pearson_max        | nan     |
+| **spearman_max**   | **nan** |
+
+<!--
+## 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: 15,488 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                       | sentence_1                                                                        | label                        |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------|
+  | type    | string                                                                           | string                                                                            | int                          |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 10.4 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.14 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
+* Samples:
+  | sentence_0                                                       | sentence_1                                                                           | label          |
+  |:-----------------------------------------------------------------|:-------------------------------------------------------------------------------------|:---------------|
+  | <code>how to use a companion certificate on delta.com</code>     | <code>SHOPPING ON DELTA.COM FOR AMEX CERT</code>                                     | <code>1</code> |
+  | <code>is jamaica can be booked with companion certificate</code> | <code>what areas can the American Express companion certificate be applied to</code> | <code>1</code> |
+  | <code>how do i book award travel on klm</code>                   | <code>can you book an air france ticket with miles</code>                            | <code>1</code> |
+* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) 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`: 12
+- `multi_dataset_batch_sampler`: round_robin
+
+#### 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
+- `num_train_epochs`: 12
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `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`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch   | Step  | Training Loss | eval_examples_spearman_max |
+|:-------:|:-----:|:-------------:|:--------------------------:|
+| 0.1033  | 100   | -             | nan                        |
+| 0.2066  | 200   | -             | nan                        |
+| 0.3099  | 300   | -             | nan                        |
+| 0.4132  | 400   | -             | nan                        |
+| 0.5165  | 500   | 0.7655        | nan                        |
+| 0.6198  | 600   | -             | nan                        |
+| 0.7231  | 700   | -             | nan                        |
+| 0.8264  | 800   | -             | nan                        |
+| 0.9298  | 900   | -             | nan                        |
+| 1.0     | 968   | -             | nan                        |
+| 1.0331  | 1000  | 0.3727        | nan                        |
+| 1.1364  | 1100  | -             | nan                        |
+| 1.2397  | 1200  | -             | nan                        |
+| 1.3430  | 1300  | -             | nan                        |
+| 1.4463  | 1400  | -             | nan                        |
+| 1.5496  | 1500  | 0.2686        | nan                        |
+| 1.6529  | 1600  | -             | nan                        |
+| 1.7562  | 1700  | -             | nan                        |
+| 1.8595  | 1800  | -             | nan                        |
+| 1.9628  | 1900  | -             | nan                        |
+| 2.0     | 1936  | -             | nan                        |
+| 2.0661  | 2000  | 0.2709        | nan                        |
+| 2.1694  | 2100  | -             | nan                        |
+| 2.2727  | 2200  | -             | nan                        |
+| 2.3760  | 2300  | -             | nan                        |
+| 2.4793  | 2400  | -             | nan                        |
+| 2.5826  | 2500  | 0.231         | nan                        |
+| 2.6860  | 2600  | -             | nan                        |
+| 2.7893  | 2700  | -             | nan                        |
+| 2.8926  | 2800  | -             | nan                        |
+| 2.9959  | 2900  | -             | nan                        |
+| 3.0     | 2904  | -             | nan                        |
+| 3.0992  | 3000  | 0.2461        | nan                        |
+| 3.2025  | 3100  | -             | nan                        |
+| 3.3058  | 3200  | -             | nan                        |
+| 3.4091  | 3300  | -             | nan                        |
+| 3.5124  | 3400  | -             | nan                        |
+| 3.6157  | 3500  | 0.2181        | nan                        |
+| 3.7190  | 3600  | -             | nan                        |
+| 3.8223  | 3700  | -             | nan                        |
+| 3.9256  | 3800  | -             | nan                        |
+| 4.0     | 3872  | -             | nan                        |
+| 4.0289  | 3900  | -             | nan                        |
+| 4.1322  | 4000  | 0.2288        | nan                        |
+| 4.2355  | 4100  | -             | nan                        |
+| 4.3388  | 4200  | -             | nan                        |
+| 4.4421  | 4300  | -             | nan                        |
+| 4.5455  | 4400  | -             | nan                        |
+| 4.6488  | 4500  | 0.2123        | nan                        |
+| 4.7521  | 4600  | -             | nan                        |
+| 4.8554  | 4700  | -             | nan                        |
+| 4.9587  | 4800  | -             | nan                        |
+| 5.0     | 4840  | -             | nan                        |
+| 5.0620  | 4900  | -             | nan                        |
+| 5.1653  | 5000  | 0.2254        | nan                        |
+| 5.2686  | 5100  | -             | nan                        |
+| 5.3719  | 5200  | -             | nan                        |
+| 5.4752  | 5300  | -             | nan                        |
+| 5.5785  | 5400  | -             | nan                        |
+| 5.6818  | 5500  | 0.2077        | nan                        |
+| 5.7851  | 5600  | -             | nan                        |
+| 5.8884  | 5700  | -             | nan                        |
+| 5.9917  | 5800  | -             | nan                        |
+| 6.0     | 5808  | -             | nan                        |
+| 6.0950  | 5900  | -             | nan                        |
+| 6.1983  | 6000  | 0.218         | nan                        |
+| 6.3017  | 6100  | -             | nan                        |
+| 6.4050  | 6200  | -             | nan                        |
+| 6.5083  | 6300  | -             | nan                        |
+| 6.6116  | 6400  | -             | nan                        |
+| 6.7149  | 6500  | 0.206         | nan                        |
+| 6.8182  | 6600  | -             | nan                        |
+| 6.9215  | 6700  | -             | nan                        |
+| 7.0     | 6776  | -             | nan                        |
+| 7.0248  | 6800  | -             | nan                        |
+| 7.1281  | 6900  | -             | nan                        |
+| 7.2314  | 7000  | 0.2126        | nan                        |
+| 7.3347  | 7100  | -             | nan                        |
+| 7.4380  | 7200  | -             | nan                        |
+| 7.5413  | 7300  | -             | nan                        |
+| 7.6446  | 7400  | -             | nan                        |
+| 7.7479  | 7500  | 0.2065        | nan                        |
+| 7.8512  | 7600  | -             | nan                        |
+| 7.9545  | 7700  | -             | nan                        |
+| 8.0     | 7744  | -             | nan                        |
+| 8.0579  | 7800  | -             | nan                        |
+| 8.1612  | 7900  | -             | nan                        |
+| 8.2645  | 8000  | 0.2068        | nan                        |
+| 8.3678  | 8100  | -             | nan                        |
+| 8.4711  | 8200  | -             | nan                        |
+| 8.5744  | 8300  | -             | nan                        |
+| 8.6777  | 8400  | -             | nan                        |
+| 8.7810  | 8500  | 0.2014        | nan                        |
+| 8.8843  | 8600  | -             | nan                        |
+| 8.9876  | 8700  | -             | nan                        |
+| 9.0     | 8712  | -             | nan                        |
+| 9.0909  | 8800  | -             | nan                        |
+| 9.1942  | 8900  | -             | nan                        |
+| 9.2975  | 9000  | 0.2057        | nan                        |
+| 9.4008  | 9100  | -             | nan                        |
+| 9.5041  | 9200  | -             | nan                        |
+| 9.6074  | 9300  | -             | nan                        |
+| 9.7107  | 9400  | -             | nan                        |
+| 9.8140  | 9500  | 0.1969        | nan                        |
+| 9.9174  | 9600  | -             | nan                        |
+| 10.0    | 9680  | -             | nan                        |
+| 10.0207 | 9700  | -             | nan                        |
+| 10.1240 | 9800  | -             | nan                        |
+| 10.2273 | 9900  | -             | nan                        |
+| 10.3306 | 10000 | 0.2023        | nan                        |
+| 10.4339 | 10100 | -             | nan                        |
+| 10.5372 | 10200 | -             | nan                        |
+| 10.6405 | 10300 | -             | nan                        |
+| 10.7438 | 10400 | -             | nan                        |
+| 10.8471 | 10500 | 0.1946        | nan                        |
+| 10.9504 | 10600 | -             | nan                        |
+| 11.0    | 10648 | -             | nan                        |
+| 11.0537 | 10700 | -             | nan                        |
+| 11.1570 | 10800 | -             | nan                        |
+| 11.2603 | 10900 | -             | nan                        |
+| 11.3636 | 11000 | 0.1982        | nan                        |
+| 11.4669 | 11100 | -             | nan                        |
+| 11.5702 | 11200 | -             | nan                        |
+| 11.6736 | 11300 | -             | nan                        |
+| 11.7769 | 11400 | -             | nan                        |
+| 11.8802 | 11500 | 0.1919        | nan                        |
+| 11.9835 | 11600 | -             | nan                        |
+| 12.0    | 11616 | -             | nan                        |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.0.0
+- Transformers: 4.41.2
+- PyTorch: 2.1.0
+- Accelerate: 0.30.1
+- Datasets: 2.19.1
+- 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",
+}
+```
+
+<!--
+## 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 @@
+{
+  "_name_or_path": "distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertModel"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.0",
+    "transformers": "4.41.2",
+    "pytorch": "2.1.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e77cbf4e242b56ab29454849ed5c7c5baca410c5952c9a264f36faab52277c69
+size 265462608

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 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+    },
+    "102": {
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+      "rstrip": false,
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+    },
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+      "rstrip": false,
+      "single_word": false,
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+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}