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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+---
+base_model: sentence-transformers/all-mpnet-base-v2
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:60
+- loss:CosineSimilarityLoss
+widget:
+- source_sentence: '#1# CLCLT00236B  - VM not ready | Total Site IDs = 1|Market Affected:  CLCLT00236B
+
+    Reported by:   Health check
+
+    Impact: UE''s roam
+
+    Full Problem Description:  CLCLT00236A - VM not ready
+
+    External Ticket: N/A
+
+    Bridge: https://meet.google.com/oab-hmxd-mqb
+
+    What groups are engaged: VMware
+
+    Next Action: Assigned the ticket to VMware'
+  sentences:
+  - Precision Time Protocol (PTP) unlocked
+  - Samsung DU Nodes not healthy
+  - VMware VM issue
+- source_sentence: '#1# -  Nodes Not Healthy, Samsung DU pods count is same as 6 |
+    Total Site IDs = 1|Reported by & Contact: Samsung Hypercare Report
+
+    Impact: UE''s will roam
+
+    Bridge:https://meet.google.com/oab-hmxd-mqb
+
+    What groups are engaged: Wireless - NOC VMware FIM, Wireless - NOCoE
+
+    Full issue description: Nodes Not Healthy, Samsung DU pods count is not 6'
+  sentences:
+  - Site Sensor temperature alert
+  - PRACH zero
+  - Samsung DU Pods not count not 6
+- source_sentence: ' - PTP Unlocked|Reported by & Contact # DU Health Check
+
+    Impact: UE''s will roam
+
+    Bridge: https://meet.google.com/oab-hmxd-qsa
+
+    What groups are engaged: NOCoE
+
+    Full issue description: -PTP Unlocked'
+  sentences:
+  - DU Health reported PTP unlocked
+  - DU PTP unlocked
+  - Physical Random access channel value is reported 0
+model-index:
+- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev
+      type: sts-dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.8503399836889165
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8646819693607537
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8610822762797875
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8632509605462457
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8627648815882912
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8646819693607537
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.8503399881242814
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8646819693607537
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8627648815882912
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8646819693607537
+      name: Spearman Max
+---
+
+# 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) on the csv dataset. 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 9a3225965996d404b775526de6dbfe85d3368642 -->
+- **Maximum Sequence Length:** 384 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - csv
+<!-- - **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}) with Transformer model: 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("yudude/all-mpnet-base-v2-sts")
+# Run inference
+sentences = [
+    " - PTP Unlocked|Reported by & Contact # DU Health Check\nImpact: UE's will roam\nBridge: https://meet.google.com/oab-hmxd-qsa\nWhat groups are engaged: NOCoE\nFull issue description: -PTP Unlocked",
+    'DU Health reported PTP unlocked',
+    'DU PTP unlocked',
+]
+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      | 0.8503     |
+| **spearman_cosine** | **0.8647** |
+| pearson_manhattan   | 0.8611     |
+| spearman_manhattan  | 0.8633     |
+| pearson_euclidean   | 0.8628     |
+| spearman_euclidean  | 0.8647     |
+| pearson_dot         | 0.8503     |
+| spearman_dot        | 0.8647     |
+| pearson_max         | 0.8628     |
+| spearman_max        | 0.8647     |
+
+<!--
+## 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
+
+#### csv
+
+* Dataset: csv
+* Size: 60 training samples
+* Columns: <code>description</code>, <code>search_key</code>, and <code>label</code>
+* Approximate statistics based on the first 60 samples:
+  |         | description                                                                          | search_key                                                                       | label                                                           |
+  |:--------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------|
+  | type    | string                                                                               | string                                                                           | float                                                           |
+  | details | <ul><li>min: 20 tokens</li><li>mean: 143.83 tokens</li><li>max: 384 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 8.75 tokens</li><li>max: 13 tokens</li></ul> | <ul><li>min: 0.9</li><li>mean: 0.95</li><li>max: 0.99</li></ul> |
+* Samples:
+  | description                                                                                                                                                           | search_key                                             | label             |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:------------------|
+  | <code>UE can not camp on network (drive test)|RU Healthcheck is okay</code>                                                                                           | <code>Network drive test shows UE cannot attach</code> | <code>0.98</code> |
+  | <code>Samsung Alert : UADPF: 513003017 (ATATL) - UADPF_513003017 - service-off at /ATATL/ATATL00725C-NR|UADPF_513003017 - service-off at /ATATL/ATATL00725C-NR</code> | <code>UADPF Service off issue</code>                   | <code>0.95</code> |
+  | <code>Samsung Alert : UADPF: 513003017 (ATATL) - UADPF_513003017 - service-off at /ATATL/ATATL00725C-NR|UADPF_513003017 - service-off at /ATATL/ATATL00725C-NR</code> | <code>Samsung UADPF service off issue</code>           | <code>0.94</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### csv
+
+* Dataset: csv
+* Size: 12 evaluation samples
+* Columns: <code>description</code>, <code>search_key</code>, and <code>label</code>
+* Approximate statistics based on the first 12 samples:
+  |         | description                                                                         | search_key                                                                       | label                                                           |
+  |:--------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                           | float                                                           |
+  | details | <ul><li>min: 32 tokens</li><li>mean: 71.67 tokens</li><li>max: 109 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 7.92 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 0.9</li><li>mean: 0.95</li><li>max: 0.99</li></ul> |
+* Samples:
+  | description                                                                                                                                                                                                               | search_key                                          | label             |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------|:------------------|
+  | <code>Temperature Sensor Fault ALERT | NJJER01462B|NJJER01462B with Temperature: Max cell ST1 29.4 | Max cell ST2  | Min cell ST1 -3276.8 | Min cell ST2 <br>Temperature: 29<br>Sitename :NJJER01462B</code>              | <code>Site Sensor temperature alert</code>          | <code>0.96</code> |
+  | <code> - PTP Unlocked|Reported by & Contact # DU Health Check<br>Impact: UE's will roam<br>Bridge: https://meet.google.com/oab-hmxd-qsa<br>What groups are engaged: NOCoE<br>Full issue description: -PTP Unlocked</code> | <code>Precision Time Protocol (PTP) unlocked</code> | <code>0.94</code> |
+  | <code> - PTP Unlocked|Reported by & Contact # DU Health Check<br>Impact: UE's will roam<br>Bridge: https://meet.google.com/oab-hmxd-qsa<br>What groups are engaged: NOCoE<br>Full issue description: -PTP Unlocked</code> | <code>DU PTP unlocked</code>                        | <code>0.96</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 4
+- `learning_rate`: 2e-05
+- `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`: 4
+- `per_device_eval_batch_size`: 4
+- `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`: 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
+- `eval_on_start`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine |
+|:------:|:----:|:-------------:|:---------------:|:-----------------------:|
+| 0.2667 | 4    | 0.2285        | 0.1834          | 0.8813                  |
+| 0.5333 | 8    | 0.1028        | 0.0760          | 0.8815                  |
+| 0.8    | 12   | 0.0409        | 0.0240          | 0.8803                  |
+| 1.0667 | 16   | 0.0235        | 0.0080          | 0.8781                  |
+| 1.3333 | 20   | 0.0077        | 0.0023          | 0.8750                  |
+| 1.6    | 24   | 0.0031        | 0.0010          | 0.8721                  |
+| 1.8667 | 28   | 0.0009        | 0.0006          | 0.8697                  |
+| 2.1333 | 32   | 0.0006        | 0.0006          | 0.8678                  |
+| 2.4    | 36   | 0.0006        | 0.0006          | 0.8667                  |
+| 2.6667 | 40   | 0.0009        | 0.0006          | 0.8660                  |
+| 2.9333 | 44   | 0.0004        | 0.0006          | 0.8654                  |
+| 3.2    | 48   | 0.0007        | 0.0006          | 0.8651                  |
+| 3.4667 | 52   | 0.0006        | 0.0006          | 0.8649                  |
+| 3.7333 | 56   | 0.0005        | 0.0006          | 0.8648                  |
+| 4.0    | 60   | 0.0003        | 0.0006          | 0.8647                  |
+| 4.2667 | 64   | 0.0007        | 0.0006          | 0.8647                  |
+| 4.5333 | 68   | 0.0005        | 0.0006          | 0.8647                  |
+| 4.8    | 72   | 0.0006        | 0.0006          | 0.8647                  |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.2.1
+- Transformers: 4.44.2
+- PyTorch: 2.5.0+cu121
+- Accelerate: 0.34.2
+- Datasets: 3.1.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "sentence-transformers/all-mpnet-base-v2",
+  "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": "4.44.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.2.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.5.0+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 sha256:667c29983c1a8244b870554b4f5c094f51f18f245a08efcacb6b8f914689154c
+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 +1,72 @@
+{
+  "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": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "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]"
+}