Add SetFit model

1_Pooling/config.json

@@ -1,10 +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
+{
+  "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

@@ -1,204 +1,224 @@
----
-library_name: setfit
-tags:
-- setfit
-- sentence-transformers
-- text-classification
-- generated_from_setfit_trainer
-metrics:
-- accuracy
-widget:
-- text: 'this is a story of two misfits who do n''t stand a chance alone , but together
-    they are magnificent . '
-- text: 'it does n''t believe in itself , it has no sense of humor ... it ''s just
-    plain bored . '
-- text: 'the band ''s courage in the face of official repression is inspiring , especially
-    for aging hippies ( this one included ) . '
-- text: 'a fast , funny , highly enjoyable movie . '
-- text: 'the movie achieves as great an impact by keeping these thoughts hidden as
-    ... ( quills ) did by showing them . '
-pipeline_tag: text-classification
-inference: true
-base_model: sentence-transformers/paraphrase-mpnet-base-v2
-model-index:
-- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
-  results:
-  - task:
-      type: text-classification
-      name: Text Classification
-    dataset:
-      name: Unknown
-      type: unknown
-      split: test
-    metrics:
-    - type: accuracy
-      value: 0.8536269430051814
-      name: Accuracy
----
-
-# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
-
-This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
-
-The model has been trained using an efficient few-shot learning technique that involves:
-
-1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
-2. Training a classification head with features from the fine-tuned Sentence Transformer.
-
-## Model Details
-
-### Model Description
-- **Model Type:** SetFit
-- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
-- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
-- **Maximum Sequence Length:** 512 tokens
-- **Number of Classes:** 2 classes
-<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
-
-### Model Sources
-
-- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
-- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
-- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
-
-### Model Labels
-| Label    | Examples                                                                                                                                                                                                                                                                                         |
-|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| negative | <ul><li>'stale and uninspired . '</li><li>"the film 's considered approach to its subject matter is too calm and thoughtful for agitprop , and the thinness of its characterizations makes it a failure as straight drama . ' "</li><li>"that their charm does n't do a load of good "</li></ul> |
-| positive | <ul><li>"broomfield is energized by volletta wallace 's maternal fury , her fearlessness "</li><li>'flawless '</li><li>'insightfully written , delicately performed '</li></ul>                                                                                                                  |
-
-## Evaluation
-
-### Metrics
-| Label   | Accuracy |
-|:--------|:---------|
-| **all** | 0.8536   |
-
-## Uses
-
-### Direct Use for Inference
-
-First install the SetFit library:
-
-```bash
-pip install setfit
-```
-
-Then you can load this model and run inference.
-
-```python
-from setfit import SetFitModel
-
-# Download from the 🤗 Hub
-model = SetFitModel.from_pretrained("majiarui/setfit-paraphrase-mpnet-base-v2-sst2")
-# Run inference
-preds = model("a fast , funny , highly enjoyable movie . ")
-```
-
-<!--
-### Downstream Use
-
-*List how someone could finetune this model on their own dataset.*
--->
-
-<!--
-### 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 Set Metrics
-| Training set | Min | Median  | Max |
-|:-------------|:----|:--------|:----|
-| Word count   | 2   | 11.4375 | 33  |
-
-| Label    | Training Sample Count |
-|:---------|:----------------------|
-| negative | 8                     |
-| positive | 8                     |
-
-### Training Hyperparameters
-- batch_size: (16, 16)
-- num_epochs: (4, 4)
-- max_steps: -1
-- sampling_strategy: oversampling
-- body_learning_rate: (2e-05, 1e-05)
-- head_learning_rate: 0.01
-- loss: CosineSimilarityLoss
-- distance_metric: cosine_distance
-- margin: 0.25
-- end_to_end: False
-- use_amp: False
-- warmup_proportion: 0.1
-- seed: 42
-- eval_max_steps: -1
-- load_best_model_at_end: True
-
-### Training Results
-| Epoch   | Step   | Training Loss | Validation Loss |
-|:-------:|:------:|:-------------:|:---------------:|
-| 0.1111  | 1      | 0.2038        | -               |
-| 1.0     | 9      | -             | 0.2198          |
-| 2.0     | 18     | -             | 0.1803          |
-| **3.0** | **27** | **-**         | **0.1788**      |
-| 4.0     | 36     | -             | 0.182           |
-
-* The bold row denotes the saved checkpoint.
-### Framework Versions
-- Python: 3.9.18
-- SetFit: 1.1.0.dev0
-- Sentence Transformers: 3.0.1
-- Transformers: 4.37.2
-- PyTorch: 2.2.0+cu121
-- Datasets: 2.17.0
-- Tokenizers: 0.15.2
-
-## Citation
-
-### BibTeX
-```bibtex
-@article{https://doi.org/10.48550/arxiv.2209.11055,
-    doi = {10.48550/ARXIV.2209.11055},
-    url = {https://arxiv.org/abs/2209.11055},
-    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
-    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
-    title = {Efficient Few-Shot Learning Without Prompts},
-    publisher = {arXiv},
-    year = {2022},
-    copyright = {Creative Commons Attribution 4.0 International}
-}
-```
-
-<!--
-## 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.*
+---
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 'this is a story of two misfits who do n''t stand a chance alone , but together
+    they are magnificent . '
+- text: 'it does n''t believe in itself , it has no sense of humor ... it ''s just
+    plain bored . '
+- text: 'the band ''s courage in the face of official repression is inspiring , especially
+    for aging hippies ( this one included ) . '
+- text: 'a fast , funny , highly enjoyable movie . '
+- text: 'the movie achieves as great an impact by keeping these thoughts hidden as
+    ... ( quills ) did by showing them . '
+inference: true
+co2_eq_emissions:
+  emissions: 12.031223883838447
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
+  ram_total_size: 125.66707992553711
+  hours_used: 0.086
+  hardware_used: 4 x NVIDIA GeForce GTX 1080
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8588082901554405
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 2 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label    | Examples                                                                                                                                                                                                                                                                                         |
+|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| negative | <ul><li>'stale and uninspired . '</li><li>"the film 's considered approach to its subject matter is too calm and thoughtful for agitprop , and the thinness of its characterizations makes it a failure as straight drama . ' "</li><li>"that their charm does n't do a load of good "</li></ul> |
+| positive | <ul><li>"broomfield is energized by volletta wallace 's maternal fury , her fearlessness "</li><li>'flawless '</li><li>'insightfully written , delicately performed '</li></ul>                                                                                                                  |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8588   |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("majiarui/setfit-paraphrase-mpnet-base-v2-sst2")
+# Run inference
+preds = model("a fast , funny , highly enjoyable movie . ")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### 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 Set Metrics
+| Training set | Min | Median  | Max |
+|:-------------|:----|:--------|:----|
+| Word count   | 2   | 11.4375 | 33  |
+
+| Label    | Training Sample Count |
+|:---------|:----------------------|
+| negative | 8                     |
+| positive | 8                     |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (4, 4)
+- max_steps: -1
+- sampling_strategy: oversampling
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch   | Step   | Training Loss | Validation Loss |
+|:-------:|:------:|:-------------:|:---------------:|
+| 0.1111  | 1      | 0.2116        | -               |
+| 1.0     | 9      | -             | 0.2229          |
+| 2.0     | 18     | -             | 0.1815          |
+| **3.0** | **27** | **-**         | **0.1729**      |
+| 4.0     | 36     | -             | 0.1752          |
+
+* The bold row denotes the saved checkpoint.
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Carbon Emitted**: 0.012 kg of CO2
+- **Hours Used**: 0.086 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 4 x NVIDIA GeForce GTX 1080
+- **CPU Model**: Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
+- **RAM Size**: 125.67 GB
+
+### Framework Versions
+- Python: 3.8.19
+- SetFit: 1.1.0.dev0
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.4
+- PyTorch: 2.3.1+cu121
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## 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

@@ -1,24 +1,24 @@
-{
-  "_name_or_path": "checkpoints\\step_27",
-  "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": "4.37.2",
-  "vocab_size": 30527
-}
+{
+  "_name_or_path": "checkpoints/step_27",
+  "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": "4.42.4",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -1,10 +1,10 @@
-{
-  "__version__": {
-    "sentence_transformers": "3.0.1",
-    "transformers": "4.37.2",
-    "pytorch": "2.2.0+cu121"
-  },
-  "prompts": {},
-  "default_prompt_name": null,
-  "similarity_fn_name": null
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.42.4",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
 }

config_setfit.json

@@ -1,7 +1,7 @@
-{
-  "labels": [
-    "negative",
-    "positive"
-  ],
-  "normalize_embeddings": false
+{
+  "labels": [
+    "negative",
+    "positive"
+  ],
+  "normalize_embeddings": false
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:737429c4e0ee96ff4e47f62eb0072590607ebbd6a52269cf2fd481c051ae30bd
+oid sha256:18e14061d4c4e16f3d717689ebf24a1607bfd11d4c42b401840f522174e65e77
 size 437967672

model_head.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3fc9318885e167df17ea9bb021419cbebb13ddbc86560638b51875c08f657b7f
-size 6949
+oid sha256:4b7128c3058c815c2fcc68a2da0ad44d1f7f2836a1e5e9950ff7a1fd7587cbb8
+size 6991

modules.json

@@ -1,14 +1,14 @@
-[
-  {
-    "idx": 0,
-    "name": "0",
-    "path": "",
-    "type": "sentence_transformers.models.Transformer"
-  },
-  {
-    "idx": 1,
-    "name": "1",
-    "path": "1_Pooling",
-    "type": "sentence_transformers.models.Pooling"
-  }
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
 ]

sentence_bert_config.json

@@ -1,4 +1,4 @@
-{
-  "max_seq_length": 512,
-  "do_lower_case": false
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
 }

special_tokens_map.json

@@ -1,51 +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
-  }
-}
+{
+  "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

@@ -1,66 +1,66 @@
-{
-  "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,
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-    },
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-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    }
-  },
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-  "clean_up_tokenization_spaces": true,
-  "cls_token": "<s>",
-  "do_basic_tokenize": true,
-  "do_lower_case": true,
-  "eos_token": "</s>",
-  "mask_token": "<mask>",
-  "max_length": 512,
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-  "tokenize_chinese_chars": true,
-  "tokenizer_class": "MPNetTokenizer",
-  "truncation_side": "right",
-  "truncation_strategy": "longest_first",
-  "unk_token": "[UNK]"
-}
+{
+  "added_tokens_decoder": {
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+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
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+      "lstrip": true,
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+      "rstrip": false,
+      "single_word": false,
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+    }
+  },
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
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+  "model_max_length": 512,
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+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
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+  "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]"
+}