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:1275
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/all-mpnet-base-v2
+widget:
+- source_sentence: snickers almond
+  sentences:
+  - Cheetos Flamin' Hot
+  - Snickers Almond
+  - Tostitos Hint of Lime
+- source_sentence: hershey's special dark
+  sentences:
+  - Hershey's Special Dark Chocolate Bar
+  - 5-Hour Energy Shot
+  - Hershey's Milk Chocolate Bar
+- source_sentence: goldfish classic
+  sentences:
+  - 3 Musketeers Bar
+  - Goldfish Crackers
+  - Hot Pockets
+- source_sentence: skittles
+  sentences:
+  - Black Tea
+  - Skittles
+  - Chips Ahoy! Chewy Cookies
+- source_sentence: cheddar cheese
+  sentences:
+  - Cucumber
+  - Cheddar Cheese Block
+  - Coffee-mate Creamer
+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 12e86a3c702fc3c50205a8db88f0ec7c0b6b94a0 -->
+- **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("Weike1000/Snack_Embed")
+# Run inference
+sentences = [
+    'cheddar cheese',
+    'Cheddar Cheese Block',
+    'Cucumber',
+]
+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, 0.9452, 0.1340],
+#         [0.9452, 1.0000, 0.1356],
+#         [0.1340, 0.1356, 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: 1,275 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                       | sentence_1                                                                      |
+  |:--------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 5.33 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.4 tokens</li><li>max: 15 tokens</li></ul> |
+* Samples:
+  | sentence_0                    | sentence_1                                      |
+  |:------------------------------|:------------------------------------------------|
+  | <code>fudge stripes</code>    | <code>Keebler Fudge Stripes Cookies</code>      |
+  | <code>gummy bears bag</code>  | <code>Gummy Bears</code>                        |
+  | <code>kind bar caramel</code> | <code>Kind Bar Caramel Almond & Sea Salt</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 1000
+- `multi_dataset_batch_sampler`: round_robin
+
+#### 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`: 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
+- `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
+- `num_train_epochs`: 1000
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `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
+- `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
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 6.25   | 500   | 0.0756        |
+| 12.5   | 1000  | 0.0396        |
+| 18.75  | 1500  | 0.033         |
+| 25.0   | 2000  | 0.0283        |
+| 31.25  | 2500  | 0.0257        |
+| 37.5   | 3000  | 0.0249        |
+| 43.75  | 3500  | 0.0248        |
+| 50.0   | 4000  | 0.019         |
+| 56.25  | 4500  | 0.0242        |
+| 62.5   | 5000  | 0.0203        |
+| 68.75  | 5500  | 0.0205        |
+| 75.0   | 6000  | 0.0225        |
+| 81.25  | 6500  | 0.0183        |
+| 87.5   | 7000  | 0.0227        |
+| 93.75  | 7500  | 0.0224        |
+| 100.0  | 8000  | 0.022         |
+| 106.25 | 8500  | 0.0244        |
+| 112.5  | 9000  | 0.0231        |
+| 118.75 | 9500  | 0.021         |
+| 125.0  | 10000 | 0.0215        |
+| 131.25 | 10500 | 0.0166        |
+| 137.5  | 11000 | 0.0186        |
+| 143.75 | 11500 | 0.0211        |
+| 150.0  | 12000 | 0.0208        |
+| 156.25 | 12500 | 0.0214        |
+| 162.5  | 13000 | 0.0207        |
+| 168.75 | 13500 | 0.0216        |
+| 175.0  | 14000 | 0.0214        |
+| 181.25 | 14500 | 0.0209        |
+| 187.5  | 15000 | 0.0197        |
+| 193.75 | 15500 | 0.022         |
+| 200.0  | 16000 | 0.0183        |
+| 206.25 | 16500 | 0.0189        |
+| 212.5  | 17000 | 0.0188        |
+| 218.75 | 17500 | 0.0163        |
+| 225.0  | 18000 | 0.0209        |
+| 231.25 | 18500 | 0.0185        |
+| 237.5  | 19000 | 0.0211        |
+| 243.75 | 19500 | 0.02          |
+| 250.0  | 20000 | 0.0206        |
+| 256.25 | 20500 | 0.0222        |
+| 262.5  | 21000 | 0.0185        |
+| 268.75 | 21500 | 0.0205        |
+| 275.0  | 22000 | 0.0165        |
+| 281.25 | 22500 | 0.0185        |
+| 287.5  | 23000 | 0.0164        |
+| 293.75 | 23500 | 0.0191        |
+| 300.0  | 24000 | 0.0197        |
+| 306.25 | 24500 | 0.0195        |
+| 312.5  | 25000 | 0.0185        |
+| 318.75 | 25500 | 0.017         |
+| 325.0  | 26000 | 0.0184        |
+| 331.25 | 26500 | 0.0184        |
+| 337.5  | 27000 | 0.0211        |
+| 343.75 | 27500 | 0.0182        |
+| 350.0  | 28000 | 0.0189        |
+| 356.25 | 28500 | 0.0172        |
+| 362.5  | 29000 | 0.0195        |
+| 368.75 | 29500 | 0.0221        |
+| 375.0  | 30000 | 0.0197        |
+| 381.25 | 30500 | 0.0228        |
+| 387.5  | 31000 | 0.0173        |
+| 393.75 | 31500 | 0.0191        |
+| 400.0  | 32000 | 0.0203        |
+| 406.25 | 32500 | 0.0202        |
+| 412.5  | 33000 | 0.0186        |
+| 418.75 | 33500 | 0.0178        |
+| 425.0  | 34000 | 0.018         |
+| 431.25 | 34500 | 0.0192        |
+| 437.5  | 35000 | 0.0186        |
+| 443.75 | 35500 | 0.0211        |
+| 450.0  | 36000 | 0.0209        |
+| 456.25 | 36500 | 0.0216        |
+| 462.5  | 37000 | 0.0201        |
+| 468.75 | 37500 | 0.0227        |
+| 475.0  | 38000 | 0.02          |
+| 481.25 | 38500 | 0.018         |
+| 487.5  | 39000 | 0.0218        |
+| 493.75 | 39500 | 0.0237        |
+| 500.0  | 40000 | 0.0208        |
+| 506.25 | 40500 | 0.0185        |
+| 512.5  | 41000 | 0.0188        |
+| 518.75 | 41500 | 0.0188        |
+| 525.0  | 42000 | 0.0168        |
+| 531.25 | 42500 | 0.017         |
+| 537.5  | 43000 | 0.0165        |
+| 543.75 | 43500 | 0.0197        |
+| 550.0  | 44000 | 0.0159        |
+| 556.25 | 44500 | 0.0224        |
+| 562.5  | 45000 | 0.0179        |
+| 568.75 | 45500 | 0.0188        |
+| 575.0  | 46000 | 0.0203        |
+| 581.25 | 46500 | 0.018         |
+| 587.5  | 47000 | 0.0195        |
+| 593.75 | 47500 | 0.0194        |
+| 600.0  | 48000 | 0.0205        |
+| 606.25 | 48500 | 0.0185        |
+| 612.5  | 49000 | 0.0208        |
+| 618.75 | 49500 | 0.0205        |
+| 625.0  | 50000 | 0.0201        |
+| 631.25 | 50500 | 0.0175        |
+| 637.5  | 51000 | 0.0171        |
+| 643.75 | 51500 | 0.0184        |
+| 650.0  | 52000 | 0.0228        |
+| 656.25 | 52500 | 0.0203        |
+| 662.5  | 53000 | 0.0222        |
+| 668.75 | 53500 | 0.0188        |
+| 675.0  | 54000 | 0.0235        |
+| 681.25 | 54500 | 0.0182        |
+| 687.5  | 55000 | 0.0215        |
+| 693.75 | 55500 | 0.018         |
+| 700.0  | 56000 | 0.0227        |
+| 706.25 | 56500 | 0.0185        |
+| 712.5  | 57000 | 0.0179        |
+| 718.75 | 57500 | 0.0176        |
+| 725.0  | 58000 | 0.0233        |
+| 731.25 | 58500 | 0.0213        |
+| 737.5  | 59000 | 0.0208        |
+| 743.75 | 59500 | 0.015         |
+| 750.0  | 60000 | 0.0199        |
+| 756.25 | 60500 | 0.0197        |
+| 762.5  | 61000 | 0.0199        |
+| 768.75 | 61500 | 0.0209        |
+| 775.0  | 62000 | 0.0185        |
+| 781.25 | 62500 | 0.0183        |
+| 787.5  | 63000 | 0.0169        |
+| 793.75 | 63500 | 0.0176        |
+| 800.0  | 64000 | 0.0206        |
+| 806.25 | 64500 | 0.0186        |
+| 812.5  | 65000 | 0.0181        |
+| 818.75 | 65500 | 0.0179        |
+| 825.0  | 66000 | 0.0184        |
+| 831.25 | 66500 | 0.0157        |
+| 837.5  | 67000 | 0.0181        |
+| 843.75 | 67500 | 0.0174        |
+| 850.0  | 68000 | 0.0185        |
+| 856.25 | 68500 | 0.0213        |
+| 862.5  | 69000 | 0.0181        |
+| 868.75 | 69500 | 0.02          |
+| 875.0  | 70000 | 0.0141        |
+| 881.25 | 70500 | 0.0168        |
+| 887.5  | 71000 | 0.0218        |
+| 893.75 | 71500 | 0.0188        |
+| 900.0  | 72000 | 0.0139        |
+| 906.25 | 72500 | 0.0188        |
+| 912.5  | 73000 | 0.022         |
+| 918.75 | 73500 | 0.0154        |
+| 925.0  | 74000 | 0.0165        |
+| 931.25 | 74500 | 0.0186        |
+| 937.5  | 75000 | 0.0191        |
+| 943.75 | 75500 | 0.0188        |
+| 950.0  | 76000 | 0.0176        |
+| 956.25 | 76500 | 0.0218        |
+| 962.5  | 77000 | 0.0185        |
+| 968.75 | 77500 | 0.0193        |
+| 975.0  | 78000 | 0.0218        |
+| 981.25 | 78500 | 0.0161        |
+| 987.5  | 79000 | 0.0216        |
+| 993.75 | 79500 | 0.0225        |
+| 1000.0 | 80000 | 0.0194        |
+
+</details>
+
+### Framework Versions
+- Python: 3.9.6
+- Sentence Transformers: 5.0.0
+- Transformers: 4.51.3
+- PyTorch: 2.7.0
+- Accelerate: 1.7.0
+- Datasets: 4.0.0
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,23 @@
+{
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "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,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.7.0"
+  },
+  "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 sha256:841a478e224f563e6737248ed994ad66189694add76c6740cd0de825ae96361b
+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]"
+}