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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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:4615
+- loss:TripletLoss
+base_model: sentence-transformers/all-mpnet-base-v2
+widget:
+- source_sentence: Do you ever feel like you have failed in life or let yourself down?
+  sentences:
+  - But I just don't feel like even getting started because I know that I will fail
+    again.
+  - I cant remember the last time I felt happiness.
+  - That was their biggest and last mistake.
+- source_sentence: Do you feel sad or unhappy?
+  sentences:
+  - I have been depressed since late September so I feel you.
+  - I share a lot of your traits, and considered myself a failure too.
+  - He conveys that feeling of regret so well I can feel it everytime
+- source_sentence: Do you feel hopeful about your future or do things seem hopeless?
+  sentences:
+  - I'm pretty optimistic though since the pace of technological growth is accelerating
+    so rapidly.
+  - '[For a clickable image, click here](http://futurism.com/thisweekinscience)
+
+
+    [To get these images directly to your inbox, sign up here](http://futurism.com/subscribe)
+
+
+    _
+
+
+    Sources | Reddit
+
+    --- | ---
+
+    [Oldest and Furthest Galaxy](http://futurism.com/links/astronomers-discover-the-oldest-and-farthest-known-galaxy/)
+    | [Reddit](https://www.reddit.com/r/science/comments/3jypyf/researchers_find_132_billion_yearold_galaxy_in/)
+
+    [3D Printed Ribs ](http://futurism.com/links/these-3d-printed-titanium-ribs-were-successfully-implanted-in-a-person/)
+    | [Reddit](https://www.reddit.com/r/technology/comments/3kj8pf/patient_receives_3dprinted_titanium_sternum_and/?ref=search_posts)
+
+    [Chinese Far Side of Moon] (http://m.phys.org/news/2015-09-china-aims-probe-moon-side.html)  |
+    [Reddit](https://www.reddit.com/r/worldnews/comments/3kcsg5/china_to_explore_dark_side_of_the_moon_china_has/)
+
+    [Rugby Ball Molecule](http://www.forbes.com/sites/carmendrahl/2015/09/02/giant-rugby-ball-new-interaction-chemistry/)
+    | [Reddit](https://www.reddit.com/r/EverythingScience/comments/3krt22/this_giant_rugby_ball_contains_a_new_chemical/)
+
+    [Measuring the Universe](http://astronomynow.com/2015/09/04/using-stellar-twins-to-climb-the-cosmic-distance-ladder/)
+    | [Reddit](https://www.reddit.com/r/science/comments/3jum8c/astronomers_have_developed_a_new_highly_accurate/)
+
+    [3D Printed Stethoscope ](http://futurism.com/links/3d-printed-stethoscopes-cost-as-little-as-2-50-and-are-just-as-good/)
+    | [Reddit](https://www.reddit.com/r/news/comments/3kgboz/doctor_3d_prints_stethoscope_to_alleviate_supply/)
+
+    [Giant Structure in Universe](http://phys.org/news/2015-09-giant-ring-like-universe.html)
+    | [Reddit](https://www.reddit.com/r/EverythingScience/comments/3jzjlm/surprising_giant_ringlike_structure_in_the/)
+
+    [Recoded Cell Factories](http://m.phys.org/news/2015-09-recoded-cells-factories-proteins.html)
+    | [Reddit](https://www.reddit.com/r/EverythingScience/comments/3krux3/researchers_transform_recoded_cells_into/)'
+  - I do not expect things to work out for me.
+- source_sentence: Do you feel sad or unhappy?
+  sentences:
+  - Me everyday im depressing
+  - And now I feel very alone and useless.
+  - Sucks that I'm not the only one because others are suffering, but it's nice to
+    know I'm not alone.
+- source_sentence: Do you feel sad or unhappy?
+  sentences:
+  - I cried because I lost not only my money, but because I lost myself.
+  - Im not exactly depressed, at least not all of the time.
+  - does anyone feel like they cant be sad
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision e8c3b32edf5434bc2275fc9bab85f82640a19130 -->
+- **Maximum Sequence Length:** 384 tokens
+- **Output Dimensionality:** 768 dimensions
+- **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': 384, 'do_lower_case': False, 'architecture': 'MPNetModel'})
+  (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})
+  (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("FritzStack/mpnet_MH_embedding")
+# Run inference
+sentences = [
+    'Do you feel sad or unhappy?',
+    'Im not exactly depressed, at least not all of the time.',
+    'does anyone feel like they cant be sad',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 1.0000,  0.7532, -0.4572],
+#         [ 0.7532,  1.0000, -0.0545],
+#         [-0.4572, -0.0545,  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
+
+#### Unnamed Dataset
+
+* Size: 4,615 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          | negative                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 9 tokens</li><li>mean: 13.63 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 20.7 tokens</li><li>max: 169 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 42.11 tokens</li><li>max: 384 tokens</li></ul> |
+* Samples:
+  | anchor                                   | positive                                  | negative                                                                                                                                                          |
+  |:-----------------------------------------|:------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Do you feel sad or unhappy?</code> | <code>I do not feel sad.</code>           | <code>I've been suffering my whole life, and it's currently at its peak :(</code>                                                                                 |
+  | <code>Do you feel sad or unhappy?</code> | <code>I feel sad much of the time.</code> | <code>Things will get better, just focus more in the positive rather than the negative</code>                                                                     |
+  | <code>Do you feel sad or unhappy?</code> | <code>I am sad all the time.</code>       | <code>That's why I understand I'm terrible, because it's wrong I get annoyed by that, people should do what they want, but I just can't stand being alone.</code> |
+* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "TripletDistanceMetric.COSINE",
+      "triplet_margin": 0.5
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 2
+- `gradient_accumulation_steps`: 8
+- `warmup_steps`: 100
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 2
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 8
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_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`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 100
+- `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
+- `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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `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`: no
+- `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`: True
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0347 | 10   | 0.3032        |
+| 0.0693 | 20   | 0.2893        |
+| 0.1040 | 30   | 0.2275        |
+| 0.1386 | 40   | 0.1532        |
+| 0.1733 | 50   | 0.1947        |
+| 0.2080 | 60   | 0.1126        |
+| 0.2426 | 70   | 0.1047        |
+| 0.2773 | 80   | 0.1118        |
+| 0.3120 | 90   | 0.0839        |
+| 0.3466 | 100  | 0.1147        |
+| 0.3813 | 110  | 0.111         |
+| 0.4159 | 120  | 0.0754        |
+| 0.4506 | 130  | 0.0964        |
+| 0.4853 | 140  | 0.1269        |
+| 0.5199 | 150  | 0.0795        |
+| 0.5546 | 160  | 0.1042        |
+| 0.5893 | 170  | 0.0797        |
+| 0.6239 | 180  | 0.0685        |
+| 0.6586 | 190  | 0.0819        |
+| 0.6932 | 200  | 0.0802        |
+| 0.7279 | 210  | 0.0934        |
+| 0.7626 | 220  | 0.0865        |
+| 0.7972 | 230  | 0.0731        |
+| 0.8319 | 240  | 0.0486        |
+| 0.8666 | 250  | 0.075         |
+| 0.9012 | 260  | 0.0627        |
+| 0.9359 | 270  | 0.0844        |
+| 0.9705 | 280  | 0.0776        |
+| 1.0035 | 290  | 0.0707        |
+| 1.0381 | 300  | 0.0479        |
+| 1.0728 | 310  | 0.05          |
+| 1.1075 | 320  | 0.0317        |
+| 1.1421 | 330  | 0.0263        |
+| 1.1768 | 340  | 0.0321        |
+| 1.2114 | 350  | 0.0221        |
+| 1.2461 | 360  | 0.0337        |
+| 1.2808 | 370  | 0.0301        |
+| 1.3154 | 380  | 0.034         |
+| 1.3501 | 390  | 0.0379        |
+| 1.3847 | 400  | 0.0489        |
+| 1.4194 | 410  | 0.0303        |
+| 1.4541 | 420  | 0.0263        |
+| 1.4887 | 430  | 0.0342        |
+| 1.5234 | 440  | 0.0328        |
+| 1.5581 | 450  | 0.0431        |
+| 1.5927 | 460  | 0.0472        |
+| 1.6274 | 470  | 0.0353        |
+| 1.6620 | 480  | 0.0389        |
+| 1.6967 | 490  | 0.0216        |
+| 1.7314 | 500  | 0.0351        |
+| 1.7660 | 510  | 0.0386        |
+| 1.8007 | 520  | 0.039         |
+| 1.8354 | 530  | 0.0264        |
+| 1.8700 | 540  | 0.0295        |
+| 1.9047 | 550  | 0.0329        |
+| 1.9393 | 560  | 0.0487        |
+| 1.9740 | 570  | 0.0287        |
+| 2.0069 | 580  | 0.0306        |
+| 2.0416 | 590  | 0.0171        |
+| 2.0763 | 600  | 0.009         |
+| 2.1109 | 610  | 0.017         |
+| 2.1456 | 620  | 0.0252        |
+| 2.1802 | 630  | 0.0123        |
+| 2.2149 | 640  | 0.0144        |
+| 2.2496 | 650  | 0.0187        |
+| 2.2842 | 660  | 0.02          |
+| 2.3189 | 670  | 0.0065        |
+| 2.3536 | 680  | 0.0131        |
+| 2.3882 | 690  | 0.0138        |
+| 2.4229 | 700  | 0.0111        |
+| 2.4575 | 710  | 0.0108        |
+| 2.4922 | 720  | 0.0079        |
+| 2.5269 | 730  | 0.0062        |
+| 2.5615 | 740  | 0.0105        |
+| 2.5962 | 750  | 0.0095        |
+| 2.6308 | 760  | 0.0112        |
+| 2.6655 | 770  | 0.0052        |
+| 2.7002 | 780  | 0.0103        |
+| 2.7348 | 790  | 0.0108        |
+| 2.7695 | 800  | 0.0059        |
+| 2.8042 | 810  | 0.0099        |
+| 2.8388 | 820  | 0.0142        |
+| 2.8735 | 830  | 0.0112        |
+| 2.9081 | 840  | 0.0194        |
+| 2.9428 | 850  | 0.0128        |
+| 2.9775 | 860  | 0.0093        |
+
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.1.1
+- Transformers: 4.57.1
+- PyTorch: 2.8.0+cu126
+- Accelerate: 1.10.1
+- Datasets: 4.0.0
+- Tokenizers: 0.22.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### TripletLoss
+```bibtex
+@misc{hermans2017defense,
+    title={In Defense of the Triplet Loss for Person Re-Identification},
+    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
+    year={2017},
+    eprint={1703.07737},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,23 @@
+{
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "transformers_version": "4.57.1",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.1",
+    "transformers": "4.57.1",
+    "pytorch": "2.8.0+cu126"
+  },
+  "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:ef38bb8c5a42bcf91b4728964a8790df04a9592ddf112364da601959296aa0ac
+size 437967672

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": 384,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "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 +1,73 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 384,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}