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1_Pooling/config.json

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+{
+    "word_embedding_dimension": 384,
+    "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:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:43059870
+- loss:CoSENTLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: baladi qeshta
+  sentences:
+  - espresso kahwah blend
+  - printed jersi
+  - baladi saucisse
+- source_sentence: mug
+  sentences:
+  - moodapex 50 mg 30 tablets, moodapex, pharmacies form tablets units 0.05 gram
+  - dark clover, long lasting flowers clover flowers dark clover flowers flowers,
+    clover flowers dark clover flowers flowers, carefully crafted with attention to
+    detail. its realistic appearance and durable materials provide a long-lasting
+    decoration for any occasion. made to enhance any space snow adds a touch of elegance
+    and beauty to your home or event.
+  - floral with belt, women dress belt dress dress floral dress, belt dress dress
+    floral dress, gender women aeilin generic dress s features belt types of fashion
+    styles casual multicolor floral
+- source_sentence: chopped vegetable dressing
+  sentences:
+  - the ski jersi
+  - carrot salad
+  - leafy green salad
+- source_sentence: monomak
+  sentences:
+  - ipad portfolio 12.9-inch size, inch ipad portfolio ipad portfolio case ipad portfolio
+    organizer ipad portfolio with size inch inch ipad carrying case ipad portfolio
+    for large screens ipad portfolio for professionals inch ipad storage case ipad
+    portfolio for business use ipad portfolio with ample space inch ipad case ipad
+    inch cover ipad inch sleeve ipad portfolio, inch ipad case ipad inch cover ipad
+    inch sleeve ipad portfolio ipad portfolio case, numeric 12.9 - inch, size 12.9-inch
+  - fine line lightening serum
+  - islamic prayer wear
+- source_sentence: classic shoes
+  sentences:
+  - forever skin cleansing device, silicone ultrasonic facial cleanser facial electric
+    cleanser ultrasonic face wash brush mini sonic face brush electric face cleanser
+    facial cleansing tool forever skin device skin cleansing device, electric face
+    cleanser facial cleansing tool forever skin device skin cleansing device, forever
+    silicone ultrasonic facial cleanser face wash brush facial electric cleanser all
+    skin type - forever offers all the benefits of deep cleansing in one compact palm-sized
+    device. the t-sonic pulsations deliver the unique ability to remove 99.5 of dirt
+    and oil as well as makeup residue and dead skin cells and exfoliate without irritating
+    the skin. just 1 minute of use twice daily cleanses and transforms the skin by
+    removing blemish-causing impurities. the mini sonic face brush is made from highly
+    durable body-safe hypoallergenic silicone and is non-porous to resist bacteria
+    build-up making it 35 x more hygienic than nylon-bristled brushes and never requiring
+    any replacement brush heads. lightweight completely waterproof for use in the
+    bath or shower and with 2 speed settings the mini is designed around your life
+    with each full charge lasting up to 300 uses. specification type skin cleansing
+    exfoliation. system power source battery. brand forever. package 1 x forever silicone
+    ultrasonic facial cleanser.
+  - v 60 ethiopian filter coffee, coffee ethiopian coffee filter coffee v 60 coffee
+    ahwa ethiopian ahwa ethiopian kahwa ethiopian kahwah ethiopian qahwa filter ahwa
+    filter kahwa filter kahwah filter qahwa kahwa kahwah qahwa v 60 ahwa v 60 ethiopian
+    filter ahwa v 60 ethiopian filter kahwa v 60 ethiopian filter kahwah v 60 ethiopian
+    filter qahwa v 60 kahwa v 60 kahwah v 60 qahwa, ethiopian filter coffee.
+  - polynomial equations calculator
+datasets:
+- KhaledReda/pairs_with_scores_v32
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# all-MiniLM-L6-v38-pair_score
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the [pairs_with_scores_v32](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v32) dataset. It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [pairs_with_scores_v32](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v32)
+- **Language:** en
+- **License:** apache-2.0
+
+### 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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
+  (1): Pooling({'word_embedding_dimension': 384, '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})
+  (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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'classic shoes',
+    'forever skin cleansing device, silicone ultrasonic facial cleanser facial electric cleanser ultrasonic face wash brush mini sonic face brush electric face cleanser facial cleansing tool forever skin device skin cleansing device, electric face cleanser facial cleansing tool forever skin device skin cleansing device, forever silicone ultrasonic facial cleanser face wash brush facial electric cleanser all skin type - forever offers all the benefits of deep cleansing in one compact palm-sized device. the t-sonic pulsations deliver the unique ability to remove 99.5 of dirt and oil as well as makeup residue and dead skin cells and exfoliate without irritating the skin. just 1 minute of use twice daily cleanses and transforms the skin by removing blemish-causing impurities. the mini sonic face brush is made from highly durable body-safe hypoallergenic silicone and is non-porous to resist bacteria build-up making it 35 x more hygienic than nylon-bristled brushes and never requiring any replacement brush heads. lightweight completely waterproof for use in the bath or shower and with 2 speed settings the mini is designed around your life with each full charge lasting up to 300 uses. specification type skin cleansing exfoliation. system power source battery. brand forever. package 1 x forever silicone ultrasonic facial cleanser.',
+    'polynomial equations calculator',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.4272, 0.5532],
+#         [0.4272, 1.0000, 0.5415],
+#         [0.5532, 0.5415, 1.0000]])
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### pairs_with_scores_v32
+
+* Dataset: [pairs_with_scores_v32](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v32) at [d05ef20](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v32/tree/d05ef20215d7229707966f04c9d3c5b3322d831e)
+* Size: 43,059,870 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                          | score                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                             | float                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 6.09 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 41.16 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.28</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                        | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | score            |
+  |:---------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>ovenware</code>            | <code>linen shirt with button down and stand up collar, men shirt long sleeves shirt men s tops button down shirt linen shirt shirt stand up collar shirt, button down shirt linen shirt shirt stand up collar shirt, gender men mix and match generic shirt s types of fashion styles casual neckline stand up collar closure style button down sleeve style long sleeves fit regular fit linen white solid occasion casual season spring summer, linen shirt with button down long sleeves and stand up collar</code>                                                                                                                                                 | <code>0.0</code> |
+  | <code>fries antipastoes</code>   | <code>tealight candle holder, home and garden home decor home decor accessory home decor accessory, rings organizer coins organizer ceramic powder holder paper holder sand holder candle holder holder home decor tealight candle holder, candle holder holder home decor tealight candle holder, create a cozy atmosphere with this tealight candle holder. not just for candles this compact holder doubles as a convenient organizer for small items like rings coins or office supplies. all our products are made of our own mixture of ceramic powder paper sand and other sustainable materials to ensure its strength and sustainability. weight 120 gm</code> | <code>0.0</code> |
+  | <code>adults bikes hybrid</code> | <code>sea salt body exfoliate and polish</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | <code>0.0</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### pairs_with_scores_v32
+
+* Dataset: [pairs_with_scores_v32](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v32) at [d05ef20](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v32/tree/d05ef20215d7229707966f04c9d3c5b3322d831e)
+* Size: 216,382 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                          | score                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                             | float                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 6.12 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 41.67 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.27</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                       | sentence2                                                                                                                                                                                                                                                                                    | score             |
+  |:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
+  | <code>cheese sauce fries</code> | <code>printed cotton scarf with fabric tassels, women scarf voile scarf shawls fabric scarf printed scarf scarf tassels scarf, fabric scarf printed scarf scarf tassels scarf, gender women mix and match generic scarf cotton black printed, printed voile scarf with fabric tassels</code> | <code>0.0</code>  |
+  | <code>camel bag</code>          | <code>camel tank top</code>                                                                                                                                                                                                                                                                  | <code>0.25</code> |
+  | <code>scrunchie</code>          | <code>spoiled babe set</code>                                                                                                                                                                                                                                                                | <code>0.75</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `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`: 128
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-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`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step   | Training Loss |
+|:------:|:------:|:-------------:|
+| 0.8505 | 286100 | 6.328         |
+| 0.8508 | 286200 | 6.4337        |
+| 0.8511 | 286300 | 6.3625        |
+| 0.8514 | 286400 | 6.3524        |
+| 0.8516 | 286500 | 6.324         |
+| 0.8519 | 286600 | 6.3453        |
+| 0.8522 | 286700 | 6.4266        |
+| 0.8525 | 286800 | 6.3666        |
+| 0.8528 | 286900 | 6.376         |
+| 0.8531 | 287000 | 6.396         |
+| 0.8534 | 287100 | 6.3725        |
+| 0.8537 | 287200 | 6.3696        |
+| 0.8540 | 287300 | 6.4024        |
+| 0.8543 | 287400 | 6.3841        |
+| 0.8546 | 287500 | 6.3344        |
+| 0.8549 | 287600 | 6.4528        |
+| 0.8552 | 287700 | 6.4161        |
+| 0.8555 | 287800 | 6.3852        |
+| 0.8558 | 287900 | 6.3908        |
+| 0.8561 | 288000 | 6.3747        |
+| 0.8564 | 288100 | 6.3385        |
+| 0.8567 | 288200 | 6.3625        |
+| 0.8570 | 288300 | 6.4054        |
+| 0.8573 | 288400 | 6.3758        |
+| 0.8576 | 288500 | 6.3604        |
+| 0.8579 | 288600 | 6.3866        |
+| 0.8582 | 288700 | 6.4301        |
+| 0.8585 | 288800 | 6.4232        |
+| 0.8588 | 288900 | 6.3781        |
+| 0.8591 | 289000 | 6.4106        |
+| 0.8594 | 289100 | 6.3579        |
+| 0.8597 | 289200 | 6.3691        |
+| 0.8600 | 289300 | 6.4222        |
+| 0.8603 | 289400 | 6.3994        |
+| 0.8606 | 289500 | 6.3615        |
+| 0.8609 | 289600 | 6.406         |
+| 0.8612 | 289700 | 6.3942        |
+| 0.8615 | 289800 | 6.3811        |
+| 0.8618 | 289900 | 6.3702        |
+| 0.8621 | 290000 | 6.3925        |
+| 0.8624 | 290100 | 6.4173        |
+| 0.8626 | 290200 | 6.4267        |
+| 0.8629 | 290300 | 6.3989        |
+| 0.8632 | 290400 | 6.3715        |
+| 0.8635 | 290500 | 6.3582        |
+| 0.8638 | 290600 | 6.3659        |
+| 0.8641 | 290700 | 6.3671        |
+| 0.8644 | 290800 | 6.3837        |
+| 0.8647 | 290900 | 6.4486        |
+| 0.8650 | 291000 | 6.3993        |
+| 0.8653 | 291100 | 6.3985        |
+| 0.8656 | 291200 | 6.3982        |
+| 0.8659 | 291300 | 6.3297        |
+| 0.8662 | 291400 | 6.3726        |
+| 0.8665 | 291500 | 6.3452        |
+| 0.8668 | 291600 | 6.3704        |
+| 0.8671 | 291700 | 6.3002        |
+| 0.8674 | 291800 | 6.4093        |
+| 0.8677 | 291900 | 6.4129        |
+| 0.8680 | 292000 | 6.4081        |
+| 0.8683 | 292100 | 6.4361        |
+| 0.8686 | 292200 | 6.4205        |
+| 0.8689 | 292300 | 6.4255        |
+| 0.8692 | 292400 | 6.4122        |
+| 0.8695 | 292500 | 6.4621        |
+| 0.8698 | 292600 | 6.364         |
+| 0.8701 | 292700 | 6.4073        |
+| 0.8704 | 292800 | 6.3409        |
+| 0.8707 | 292900 | 6.3107        |
+| 0.8710 | 293000 | 6.3727        |
+| 0.8713 | 293100 | 6.3447        |
+| 0.8716 | 293200 | 6.4191        |
+| 0.8719 | 293300 | 6.3492        |
+| 0.8722 | 293400 | 6.3553        |
+| 0.8725 | 293500 | 6.3768        |
+| 0.8728 | 293600 | 6.3378        |
+| 0.8731 | 293700 | 6.3998        |
+| 0.8733 | 293800 | 6.438         |
+| 0.8736 | 293900 | 6.34          |
+| 0.8739 | 294000 | 6.4061        |
+| 0.8742 | 294100 | 6.4552        |
+| 0.8745 | 294200 | 6.2997        |
+| 0.8748 | 294300 | 6.4018        |
+| 0.8751 | 294400 | 6.412         |
+| 0.8754 | 294500 | 6.3756        |
+| 0.8757 | 294600 | 6.3983        |
+| 0.8760 | 294700 | 6.3758        |
+| 0.8763 | 294800 | 6.3707        |
+| 0.8766 | 294900 | 6.3802        |
+| 0.8769 | 295000 | 6.3767        |
+| 0.8772 | 295100 | 6.4037        |
+| 0.8775 | 295200 | 6.3425        |
+| 0.8778 | 295300 | 6.3655        |
+| 0.8781 | 295400 | 6.4575        |
+| 0.8784 | 295500 | 6.4242        |
+| 0.8787 | 295600 | 6.365         |
+| 0.8790 | 295700 | 6.373         |
+| 0.8793 | 295800 | 6.3766        |
+| 0.8796 | 295900 | 6.3835        |
+| 0.8799 | 296000 | 6.4327        |
+| 0.8802 | 296100 | 6.3799        |
+| 0.8805 | 296200 | 6.41          |
+| 0.8808 | 296300 | 6.3092        |
+| 0.8811 | 296400 | 6.4133        |
+| 0.8814 | 296500 | 6.3952        |
+| 0.8817 | 296600 | 6.3937        |
+| 0.8820 | 296700 | 6.3204        |
+| 0.8823 | 296800 | 6.4072        |
+| 0.8826 | 296900 | 6.3577        |
+| 0.8829 | 297000 | 6.3966        |
+| 0.8832 | 297100 | 6.3906        |
+| 0.8835 | 297200 | 6.3871        |
+| 0.8838 | 297300 | 6.3546        |
+| 0.8841 | 297400 | 6.3874        |
+| 0.8843 | 297500 | 6.4042        |
+| 0.8846 | 297600 | 6.3963        |
+| 0.8849 | 297700 | 6.3708        |
+| 0.8852 | 297800 | 6.3269        |
+| 0.8855 | 297900 | 6.3554        |
+| 0.8858 | 298000 | 6.3884        |
+| 0.8861 | 298100 | 6.3645        |
+| 0.8864 | 298200 | 6.4203        |
+| 0.8867 | 298300 | 6.3827        |
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+| 0.8885 | 298900 | 6.3643        |
+| 0.8888 | 299000 | 6.3606        |
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+| 0.8897 | 299300 | 6.3836        |
+| 0.8900 | 299400 | 6.3696        |
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+| 0.8906 | 299600 | 6.3142        |
+| 0.8909 | 299700 | 6.3703        |
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+| 0.8915 | 299900 | 6.3847        |
+| 0.8918 | 300000 | 6.3761        |
+| 0.8921 | 300100 | 6.3673        |
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+| 0.8927 | 300300 | 6.4366        |
+| 0.8930 | 300400 | 6.3626        |
+| 0.8933 | 300500 | 6.3549        |
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+| 0.8945 | 300900 | 6.3447        |
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+| 0.8950 | 301100 | 6.3719        |
+| 0.8953 | 301200 | 6.4033        |
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+| 0.8962 | 301500 | 6.3273        |
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+| 0.8974 | 301900 | 6.4151        |
+| 0.8977 | 302000 | 6.3435        |
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+| 0.8983 | 302200 | 6.3563        |
+| 0.8986 | 302300 | 6.3999        |
+| 0.8989 | 302400 | 6.349         |
+| 0.8992 | 302500 | 6.3886        |
+| 0.8995 | 302600 | 6.387         |
+| 0.8998 | 302700 | 6.3786        |
+| 0.9001 | 302800 | 6.4126        |
+| 0.9004 | 302900 | 6.3439        |
+| 0.9007 | 303000 | 6.3376        |
+| 0.9010 | 303100 | 6.3512        |
+| 0.9013 | 303200 | 6.4281        |
+| 0.9016 | 303300 | 6.3999        |
+| 0.9019 | 303400 | 6.3757        |
+| 0.9022 | 303500 | 6.3297        |
+| 0.9025 | 303600 | 6.4042        |
+| 0.9028 | 303700 | 6.3001        |
+| 0.9031 | 303800 | 6.3028        |
+| 0.9034 | 303900 | 6.3969        |
+| 0.9037 | 304000 | 6.2983        |
+| 0.9040 | 304100 | 6.3043        |
+| 0.9043 | 304200 | 6.4063        |
+| 0.9046 | 304300 | 6.3829        |
+| 0.9049 | 304400 | 6.3786        |
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+| 0.9084 | 305600 | 6.3175        |
+| 0.9087 | 305700 | 6.3624        |
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+| 0.9093 | 305900 | 6.4152        |
+| 0.9096 | 306000 | 6.4059        |
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+| 0.9102 | 306200 | 6.3976        |
+| 0.9105 | 306300 | 6.3498        |
+| 0.9108 | 306400 | 6.3638        |
+| 0.9111 | 306500 | 6.4264        |
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+| 0.9120 | 306800 | 6.3601        |
+| 0.9123 | 306900 | 6.3875        |
+| 0.9126 | 307000 | 6.4401        |
+| 0.9129 | 307100 | 6.3931        |
+| 0.9132 | 307200 | 6.3875        |
+| 0.9135 | 307300 | 6.3293        |
+| 0.9138 | 307400 | 6.3539        |
+| 0.9141 | 307500 | 6.3619        |
+| 0.9144 | 307600 | 6.364         |
+| 0.9147 | 307700 | 6.4567        |
+| 0.9150 | 307800 | 6.393         |
+| 0.9153 | 307900 | 6.4153        |
+| 0.9156 | 308000 | 6.3644        |
+| 0.9159 | 308100 | 6.3899        |
+| 0.9162 | 308200 | 6.3986        |
+| 0.9165 | 308300 | 6.3766        |
+| 0.9167 | 308400 | 6.4279        |
+| 0.9170 | 308500 | 6.3578        |
+| 0.9173 | 308600 | 6.3891        |
+| 0.9176 | 308700 | 6.3029        |
+| 0.9179 | 308800 | 6.3688        |
+| 0.9182 | 308900 | 6.3787        |
+| 0.9185 | 309000 | 6.3935        |
+| 0.9188 | 309100 | 6.4319        |
+| 0.9191 | 309200 | 6.2945        |
+| 0.9194 | 309300 | 6.3871        |
+| 0.9197 | 309400 | 6.3338        |
+| 0.9200 | 309500 | 6.3654        |
+| 0.9203 | 309600 | 6.4207        |
+| 0.9206 | 309700 | 6.3809        |
+| 0.9209 | 309800 | 6.3798        |
+| 0.9212 | 309900 | 6.3974        |
+| 0.9215 | 310000 | 6.334         |
+| 0.9218 | 310100 | 6.376         |
+| 0.9221 | 310200 | 6.3939        |
+| 0.9224 | 310300 | 6.4144        |
+| 0.9227 | 310400 | 6.4375        |
+| 0.9230 | 310500 | 6.316         |
+| 0.9233 | 310600 | 6.3346        |
+| 0.9236 | 310700 | 6.3766        |
+| 0.9239 | 310800 | 6.3564        |
+| 0.9242 | 310900 | 6.3643        |
+| 0.9245 | 311000 | 6.3627        |
+| 0.9248 | 311100 | 6.4283        |
+| 0.9251 | 311200 | 6.3179        |
+| 0.9254 | 311300 | 6.4113        |
+| 0.9257 | 311400 | 6.3703        |
+| 0.9260 | 311500 | 6.3388        |
+| 0.9263 | 311600 | 6.3997        |
+| 0.9266 | 311700 | 6.3813        |
+| 0.9269 | 311800 | 6.3723        |
+| 0.9272 | 311900 | 6.3556        |
+| 0.9275 | 312000 | 6.3522        |
+| 0.9277 | 312100 | 6.3661        |
+| 0.9280 | 312200 | 6.405         |
+| 0.9283 | 312300 | 6.4031        |
+| 0.9286 | 312400 | 6.4125        |
+| 0.9289 | 312500 | 6.3225        |
+| 0.9292 | 312600 | 6.3887        |
+| 0.9295 | 312700 | 6.3368        |
+| 0.9298 | 312800 | 6.3323        |
+| 0.9301 | 312900 | 6.4433        |
+| 0.9304 | 313000 | 6.4155        |
+| 0.9307 | 313100 | 6.3448        |
+| 0.9310 | 313200 | 6.3775        |
+| 0.9313 | 313300 | 6.3736        |
+| 0.9316 | 313400 | 6.3611        |
+| 0.9319 | 313500 | 6.3988        |
+| 0.9322 | 313600 | 6.3243        |
+| 0.9325 | 313700 | 6.4137        |
+| 0.9328 | 313800 | 6.3663        |
+| 0.9331 | 313900 | 6.3742        |
+| 0.9334 | 314000 | 6.4021        |
+| 0.9337 | 314100 | 6.4171        |
+| 0.9340 | 314200 | 6.3948        |
+| 0.9343 | 314300 | 6.3916        |
+| 0.9346 | 314400 | 6.365         |
+| 0.9349 | 314500 | 6.3479        |
+| 0.9352 | 314600 | 6.3588        |
+| 0.9355 | 314700 | 6.3247        |
+| 0.9358 | 314800 | 6.3584        |
+| 0.9361 | 314900 | 6.3436        |
+| 0.9364 | 315000 | 6.3958        |
+| 0.9367 | 315100 | 6.3424        |
+| 0.9370 | 315200 | 6.3814        |
+| 0.9373 | 315300 | 6.3612        |
+| 0.9376 | 315400 | 6.3889        |
+| 0.9379 | 315500 | 6.3591        |
+| 0.9382 | 315600 | 6.3856        |
+| 0.9384 | 315700 | 6.3594        |
+| 0.9387 | 315800 | 6.3737        |
+| 0.9390 | 315900 | 6.4489        |
+| 0.9393 | 316000 | 6.2902        |
+| 0.9396 | 316100 | 6.3517        |
+| 0.9399 | 316200 | 6.4662        |
+| 0.9402 | 316300 | 6.3684        |
+| 0.9405 | 316400 | 6.362         |
+| 0.9408 | 316500 | 6.3492        |
+| 0.9411 | 316600 | 6.4018        |
+| 0.9414 | 316700 | 6.3709        |
+| 0.9417 | 316800 | 6.4048        |
+| 0.9420 | 316900 | 6.3547        |
+| 0.9423 | 317000 | 6.2638        |
+| 0.9426 | 317100 | 6.435         |
+| 0.9429 | 317200 | 6.4028        |
+| 0.9432 | 317300 | 6.39          |
+| 0.9435 | 317400 | 6.3688        |
+| 0.9438 | 317500 | 6.3801        |
+| 0.9441 | 317600 | 6.3609        |
+| 0.9444 | 317700 | 6.3583        |
+| 0.9447 | 317800 | 6.3339        |
+| 0.9450 | 317900 | 6.3804        |
+| 0.9453 | 318000 | 6.3718        |
+| 0.9456 | 318100 | 6.3434        |
+| 0.9459 | 318200 | 6.3765        |
+| 0.9462 | 318300 | 6.3468        |
+| 0.9465 | 318400 | 6.3253        |
+| 0.9468 | 318500 | 6.3868        |
+| 0.9471 | 318600 | 6.3906        |
+| 0.9474 | 318700 | 6.4371        |
+| 0.9477 | 318800 | 6.3737        |
+| 0.9480 | 318900 | 6.3332        |
+| 0.9483 | 319000 | 6.3698        |
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+| 0.9489 | 319200 | 6.4309        |
+| 0.9492 | 319300 | 6.3757        |
+| 0.9494 | 319400 | 6.3615        |
+| 0.9497 | 319500 | 6.366         |
+| 0.9500 | 319600 | 6.3574        |
+| 0.9503 | 319700 | 6.3742        |
+| 0.9506 | 319800 | 6.3461        |
+| 0.9509 | 319900 | 6.3063        |
+| 0.9512 | 320000 | 6.3504        |
+| 0.9515 | 320100 | 6.4292        |
+| 0.9518 | 320200 | 6.3603        |
+| 0.9521 | 320300 | 6.3664        |
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+| 0.9527 | 320500 | 6.3696        |
+| 0.9530 | 320600 | 6.4512        |
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+| 0.9536 | 320800 | 6.319         |
+| 0.9539 | 320900 | 6.3873        |
+| 0.9542 | 321000 | 6.4429        |
+| 0.9545 | 321100 | 6.4334        |
+| 0.9548 | 321200 | 6.3168        |
+| 0.9551 | 321300 | 6.4112        |
+| 0.9554 | 321400 | 6.4135        |
+| 0.9557 | 321500 | 6.3718        |
+| 0.9560 | 321600 | 6.393         |
+| 0.9563 | 321700 | 6.331         |
+| 0.9566 | 321800 | 6.3811        |
+| 0.9569 | 321900 | 6.3748        |
+| 0.9572 | 322000 | 6.4013        |
+| 0.9575 | 322100 | 6.3281        |
+| 0.9578 | 322200 | 6.3634        |
+| 0.9581 | 322300 | 6.3473        |
+| 0.9584 | 322400 | 6.3429        |
+| 0.9587 | 322500 | 6.3837        |
+| 0.9590 | 322600 | 6.3855        |
+| 0.9593 | 322700 | 6.3825        |
+| 0.9596 | 322800 | 6.4182        |
+| 0.9599 | 322900 | 6.3611        |
+| 0.9601 | 323000 | 6.4276        |
+| 0.9604 | 323100 | 6.3329        |
+| 0.9607 | 323200 | 6.3764        |
+| 0.9610 | 323300 | 6.3382        |
+| 0.9613 | 323400 | 6.3084        |
+| 0.9616 | 323500 | 6.3884        |
+| 0.9619 | 323600 | 6.3733        |
+| 0.9622 | 323700 | 6.3145        |
+| 0.9625 | 323800 | 6.4082        |
+| 0.9628 | 323900 | 6.2616        |
+| 0.9631 | 324000 | 6.3564        |
+| 0.9634 | 324100 | 6.4159        |
+| 0.9637 | 324200 | 6.3898        |
+| 0.9640 | 324300 | 6.3522        |
+| 0.9643 | 324400 | 6.3905        |
+| 0.9646 | 324500 | 6.3628        |
+| 0.9649 | 324600 | 6.3219        |
+| 0.9652 | 324700 | 6.4094        |
+| 0.9655 | 324800 | 6.4043        |
+| 0.9658 | 324900 | 6.405         |
+| 0.9661 | 325000 | 6.3272        |
+| 0.9664 | 325100 | 6.3852        |
+| 0.9667 | 325200 | 6.4279        |
+| 0.9670 | 325300 | 6.385         |
+| 0.9673 | 325400 | 6.432         |
+| 0.9676 | 325500 | 6.4317        |
+| 0.9679 | 325600 | 6.3754        |
+| 0.9682 | 325700 | 6.4305        |
+| 0.9685 | 325800 | 6.313         |
+| 0.9688 | 325900 | 6.3338        |
+| 0.9691 | 326000 | 6.4271        |
+| 0.9694 | 326100 | 6.4092        |
+| 0.9697 | 326200 | 6.3071        |
+| 0.9700 | 326300 | 6.3712        |
+| 0.9703 | 326400 | 6.3486        |
+| 0.9706 | 326500 | 6.3041        |
+| 0.9709 | 326600 | 6.3464        |
+| 0.9711 | 326700 | 6.3351        |
+| 0.9714 | 326800 | 6.3166        |
+| 0.9717 | 326900 | 6.3343        |
+| 0.9720 | 327000 | 6.403         |
+| 0.9723 | 327100 | 6.3923        |
+| 0.9726 | 327200 | 6.4203        |
+| 0.9729 | 327300 | 6.3716        |
+| 0.9732 | 327400 | 6.3341        |
+| 0.9735 | 327500 | 6.3253        |
+| 0.9738 | 327600 | 6.3648        |
+| 0.9741 | 327700 | 6.4148        |
+| 0.9744 | 327800 | 6.3431        |
+| 0.9747 | 327900 | 6.3149        |
+| 0.9750 | 328000 | 6.3697        |
+| 0.9753 | 328100 | 6.3777        |
+| 0.9756 | 328200 | 6.3446        |
+| 0.9759 | 328300 | 6.3484        |
+| 0.9762 | 328400 | 6.3118        |
+| 0.9765 | 328500 | 6.3657        |
+| 0.9768 | 328600 | 6.4045        |
+| 0.9771 | 328700 | 6.3776        |
+| 0.9774 | 328800 | 6.3609        |
+| 0.9777 | 328900 | 6.3024        |
+| 0.9780 | 329000 | 6.4298        |
+| 0.9783 | 329100 | 6.3598        |
+| 0.9786 | 329200 | 6.3555        |
+| 0.9789 | 329300 | 6.3915        |
+| 0.9792 | 329400 | 6.3807        |
+| 0.9795 | 329500 | 6.2983        |
+| 0.9798 | 329600 | 6.371         |
+| 0.9801 | 329700 | 6.3647        |
+| 0.9804 | 329800 | 6.3892        |
+| 0.9807 | 329900 | 6.3543        |
+| 0.9810 | 330000 | 6.4178        |
+| 0.9813 | 330100 | 6.3228        |
+| 0.9816 | 330200 | 6.3684        |
+| 0.9818 | 330300 | 6.3711        |
+| 0.9821 | 330400 | 6.3717        |
+| 0.9824 | 330500 | 6.3976        |
+| 0.9827 | 330600 | 6.3483        |
+| 0.9830 | 330700 | 6.335         |
+| 0.9833 | 330800 | 6.385         |
+| 0.9836 | 330900 | 6.3772        |
+| 0.9839 | 331000 | 6.3027        |
+| 0.9842 | 331100 | 6.3634        |
+| 0.9845 | 331200 | 6.3261        |
+| 0.9848 | 331300 | 6.3708        |
+| 0.9851 | 331400 | 6.3993        |
+| 0.9854 | 331500 | 6.3759        |
+| 0.9857 | 331600 | 6.3485        |
+| 0.9860 | 331700 | 6.3717        |
+| 0.9863 | 331800 | 6.3776        |
+| 0.9866 | 331900 | 6.4366        |
+| 0.9869 | 332000 | 6.4023        |
+| 0.9872 | 332100 | 6.3978        |
+| 0.9875 | 332200 | 6.3382        |
+| 0.9878 | 332300 | 6.3474        |
+| 0.9881 | 332400 | 6.4122        |
+| 0.9884 | 332500 | 6.3809        |
+| 0.9887 | 332600 | 6.322         |
+| 0.9890 | 332700 | 6.344         |
+| 0.9893 | 332800 | 6.2637        |
+| 0.9896 | 332900 | 6.4016        |
+| 0.9899 | 333000 | 6.3826        |
+| 0.9902 | 333100 | 6.4467        |
+| 0.9905 | 333200 | 6.4596        |
+| 0.9908 | 333300 | 6.3065        |
+| 0.9911 | 333400 | 6.4057        |
+| 0.9914 | 333500 | 6.435         |
+| 0.9917 | 333600 | 6.3398        |
+| 0.9920 | 333700 | 6.3741        |
+| 0.9923 | 333800 | 6.3069        |
+| 0.9926 | 333900 | 6.3457        |
+| 0.9928 | 334000 | 6.3884        |
+| 0.9931 | 334100 | 6.4078        |
+| 0.9934 | 334200 | 6.3242        |
+| 0.9937 | 334300 | 6.3621        |
+| 0.9940 | 334400 | 6.3515        |
+| 0.9943 | 334500 | 6.4017        |
+| 0.9946 | 334600 | 6.4629        |
+| 0.9949 | 334700 | 6.3686        |
+| 0.9952 | 334800 | 6.3224        |
+| 0.9955 | 334900 | 6.386         |
+| 0.9958 | 335000 | 6.3899        |
+| 0.9961 | 335100 | 6.3488        |
+| 0.9964 | 335200 | 6.4117        |
+| 0.9967 | 335300 | 6.3988        |
+| 0.9970 | 335400 | 6.3536        |
+| 0.9973 | 335500 | 6.3861        |
+| 0.9976 | 335600 | 6.3383        |
+| 0.9979 | 335700 | 6.3848        |
+| 0.9982 | 335800 | 6.4582        |
+| 0.9985 | 335900 | 6.3452        |
+| 0.9988 | 336000 | 6.3651        |
+| 0.9991 | 336100 | 6.3704        |
+| 0.9994 | 336200 | 6.3801        |
+| 0.9997 | 336300 | 6.3701        |
+| 1.0000 | 336400 | 6.4452        |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.1.0
+- Transformers: 4.55.4
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.10.1
+- Datasets: 4.0.0
+- Tokenizers: 0.21.4
+
+## 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",
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.55.4",
+    "pytorch": "2.6.0+cu124"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b728441a78d9a166a21e80d1e6df0783bcdd569889dabf6d66c4b18b057b6a07
+size 90864192

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": 256,
+    "do_lower_case": false
+}

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 +1,65 @@
+{
+  "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": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}