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:10000
+- loss:SoftmaxLoss
+base_model: google-bert/bert-base-uncased
+datasets: []
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+widget:
+- source_sentence: A man selling donuts to a customer during a world exhibition event
+    held in the city of Angeles
+  sentences:
+  - The man is doing tricks.
+  - A woman drinks her coffee in a small cafe.
+  - The building is made of logs.
+- source_sentence: A group of people prepare hot air balloons for takeoff.
+  sentences:
+  - There are hot air balloons on the ground and air.
+  - A man is in an art museum.
+  - People watch another person do a trick.
+- source_sentence: Three workers are trimming down trees.
+  sentences:
+  - The goalie is sleeping at home.
+  - There are three workers
+  - The girl has brown hair.
+- source_sentence: Two brown-haired men wearing short-sleeved shirts and shorts are
+    climbing stairs.
+  sentences:
+  - The men have blonde hair.
+  - A bicyclist passes an esthetically beautiful building on a sunny day
+  - Two men are dancing.
+- source_sentence: A man is sitting in on the side of the street with brass pots.
+  sentences:
+  - a younger boy looks at his father
+  - Children are at the beach.
+  - a man does not have brass pots
+pipeline_tag: sentence-similarity
+co2_eq_emissions:
+  emissions: 147.28843774992524
+  energy_consumed: 0.2758298255748315
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: AMD EPYC 7H12 64-Core Processor
+  ram_total_size: 229.14864349365234
+  hours_used: 0.351
+  hardware_used: 8 x NVIDIA GeForce RTX 3090
+model-index:
+- name: SentenceTransformer based on google-bert/bert-base-uncased
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev
+      type: sts-dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.47725003430658275
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.5475746919034576
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.5043805022296893
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.5420702830995872
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.5083739540394052
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.544209699690841
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.4458579859528435
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.4698642508787034
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.5083739540394052
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.5475746919034576
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test
+      type: sts-test
+    metrics:
+    - type: pearson_cosine
+      value: 0.5320947494943107
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.5317279446221387
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.5575308236485216
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.5554390408837996
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.55587770863865
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.5535804159700501
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.2787697886285483
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.2710358104528421
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.5575308236485216
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.5554390408837996
+      name: Spearman Max
+    - type: pearson_cosine
+      value: 0.4493844540252116
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.4694611677633312
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.4773641092320219
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.4763054309792941
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.4796801942910325
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.47774521406648734
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.4081600817978359
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.3898881150281674
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.4796801942910325
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.47774521406648734
+      name: Spearman Max
+---
+
+# SentenceTransformer based on google-bert/bert-base-uncased
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-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:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
+- **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("jilangdi/bert-base-uncased-nli-v1")
+# Run inference
+sentences = [
+    'A man is sitting in on the side of the street with brass pots.',
+    'a man does not have brass pots',
+    'Children are at the beach.',
+]
+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: `sts-dev`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.4773     |
+| **spearman_cosine** | **0.5476** |
+| pearson_manhattan   | 0.5044     |
+| spearman_manhattan  | 0.5421     |
+| pearson_euclidean   | 0.5084     |
+| spearman_euclidean  | 0.5442     |
+| pearson_dot         | 0.4459     |
+| spearman_dot        | 0.4699     |
+| pearson_max         | 0.5084     |
+| spearman_max        | 0.5476     |
+
+#### Semantic Similarity
+* Dataset: `sts-test`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.5321     |
+| **spearman_cosine** | **0.5317** |
+| pearson_manhattan   | 0.5575     |
+| spearman_manhattan  | 0.5554     |
+| pearson_euclidean   | 0.5559     |
+| spearman_euclidean  | 0.5536     |
+| pearson_dot         | 0.2788     |
+| spearman_dot        | 0.271      |
+| pearson_max         | 0.5575     |
+| spearman_max        | 0.5554     |
+
+#### Semantic Similarity
+* Dataset: `sts-test`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.4494     |
+| **spearman_cosine** | **0.4695** |
+| pearson_manhattan   | 0.4774     |
+| spearman_manhattan  | 0.4763     |
+| pearson_euclidean   | 0.4797     |
+| spearman_euclidean  | 0.4777     |
+| pearson_dot         | 0.4082     |
+| spearman_dot        | 0.3899     |
+| pearson_max         | 0.4797     |
+| spearman_max        | 0.4777     |
+
+<!--
+## 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: 10,000 training samples
+* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | premise                                                                           | hypothesis                                                                       | label                                                              |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | int                                                                |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 17.38 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.7 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>0: ~33.40%</li><li>1: ~33.30%</li><li>2: ~33.30%</li></ul> |
+* Samples:
+  | premise                                                             | hypothesis                                                     | label          |
+  |:--------------------------------------------------------------------|:---------------------------------------------------------------|:---------------|
+  | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is training his horse for a competition.</code> | <code>1</code> |
+  | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is at a diner, ordering an omelette.</code>     | <code>2</code> |
+  | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code>                 | <code>0</code> |
+* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 1,000 evaluation samples
+* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | premise                                                                           | hypothesis                                                                        | label                                                              |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | int                                                                |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 18.44 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.57 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>0: ~33.10%</li><li>1: ~33.30%</li><li>2: ~33.60%</li></ul> |
+* Samples:
+  | premise                                                            | hypothesis                                                                                         | label          |
+  |:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------|
+  | <code>Two women are embracing while holding to go packages.</code> | <code>The sisters are hugging goodbye while holding to go packages after just eating lunch.</code> | <code>1</code> |
+  | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code>                                                       | <code>0</code> |
+  | <code>Two women are embracing while holding to go packages.</code> | <code>The men are fighting outside a deli.</code>                                                  | <code>2</code> |
+* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 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`: 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`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | loss   | sts-dev_spearman_cosine | sts-test_spearman_cosine |
+|:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:|
+| 0      | 0    | -             | -      | 0.5931                  | -                        |
+| 1.0    | 79   | -             | -      | -                       | 0.5317                   |
+| 1.2658 | 100  | 0.545         | 0.9351 | 0.5973                  | -                        |
+| 2.5316 | 200  | 0.5286        | 0.9535 | 0.5660                  | -                        |
+| 3.7975 | 300  | 0.3553        | 1.0364 | 0.5476                  | -                        |
+| 5.0    | 395  | -             | -      | -                       | 0.4695                   |
+
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Energy Consumed**: 0.276 kWh
+- **Carbon Emitted**: 0.147 kg of CO2
+- **Hours Used**: 0.351 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 8 x NVIDIA GeForce RTX 3090
+- **CPU Model**: AMD EPYC 7H12 64-Core Processor
+- **RAM Size**: 229.15 GB
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.3.1+cu121
+- Accelerate: 0.31.0
+- Datasets: 2.19.2
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers and SoftmaxLoss
+```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 +1,26 @@
+{
+  "_name_or_path": "google-bert/bert-base-uncased",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "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": "4.41.2",
+    "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:d424cea33c6f3cc78e6500be83f714817032e7da35c2fa879028fdda93859465
+size 437951328

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,
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+  "cls_token": "[CLS]",
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+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}