Push model using huggingface_hub.
Browse files- 1_Pooling/config.json +9 -0
- README.md +130 -0
- config.json +24 -0
- config_sentence_transformers.json +7 -0
- config_setfit.json +7 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +51 -0
- tokenizer.json +0 -0
- tokenizer_config.json +66 -0
- vocab.txt +0 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false
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}
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README.md
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---
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library_name: setfit
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tags:
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- setfit
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- sentence-transformers
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- text-classification
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- generated_from_setfit_trainer
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metrics:
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- accuracy
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widget: []
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pipeline_tag: text-classification
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inference: true
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---
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# SetFit
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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The model has been trained using an efficient few-shot learning technique that involves:
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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2. Training a classification head with features from the fine-tuned Sentence Transformer.
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## Model Details
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### Model Description
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- **Model Type:** SetFit
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<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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- **Maximum Sequence Length:** 512 tokens
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- **Number of Classes:** 2 classes
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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## Uses
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### Direct Use for Inference
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First install the SetFit library:
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```bash
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pip install setfit
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```
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Then you can load this model and run inference.
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```python
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from setfit import SetFitModel
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("setfit_model_id")
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# Run inference
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preds = model("I loved the spiderman movie!")
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```
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<!--
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### Downstream Use
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+
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*List how someone could finetune this model on their own dataset.*
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-->
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+
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+
-->
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+
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<!--
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## Bias, Risks and Limitations
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+
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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+
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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+
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## Training Details
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### Framework Versions
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- Python: 3.11.7
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- SetFit: 1.0.3
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- Sentence Transformers: 2.3.1
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- Transformers: 4.37.2
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- PyTorch: 2.2.0
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- Datasets: 2.16.1
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- Tokenizers: 0.15.1
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## Citation
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### BibTeX
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```bibtex
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@article{https://doi.org/10.48550/arxiv.2209.11055,
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doi = {10.48550/ARXIV.2209.11055},
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url = {https://arxiv.org/abs/2209.11055},
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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title = {Efficient Few-Shot Learning Without Prompts},
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publisher = {arXiv},
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year = {2022},
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copyright = {Creative Commons Attribution 4.0 International}
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}
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```
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<!--
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## Glossary
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*Clearly define terms in order to be accessible across audiences.*
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-->
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<!--
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## Model Card Authors
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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-->
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<!--
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## Model Card Contact
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|
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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-->
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config.json
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{
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"_name_or_path": "./models/sf-bin-multi-qa-payment-terms",
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"architectures": [
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"MPNetModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "mpnet",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"relative_attention_num_buckets": 32,
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"torch_dtype": "float32",
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"transformers_version": "4.37.2",
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"vocab_size": 30527
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}
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.0.0",
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"transformers": "4.7.0",
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"pytorch": "1.9.0+cu102"
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}
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}
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config_setfit.json
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{
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"normalize_embeddings": false,
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"labels": [
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"negative",
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"positive"
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]
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:ff3187592337e0175d03ca82443f1dcd7449b237f64dd0bbd5e5745e218c4072
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size 437967672
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model_head.pkl
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:39c1653da166b28f93cb8441f00ad98df3aca459de8ae656fcb6078734578a80
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size 7007
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modules.json
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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}
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]
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sentence_bert_config.json
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{
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"max_seq_length": 512,
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"do_lower_case": false
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}
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"cls_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": {
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"content": "<mask>",
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"lstrip": true,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"sep_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
|
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"single_word": false
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},
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"unk_token": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "<s>",
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5 |
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"lstrip": false,
|
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"normalized": false,
|
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": false,
|
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"rstrip": false,
|
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"single_word": false,
|
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"special": true
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},
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"2": {
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"content": "</s>",
|
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+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
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"rstrip": false,
|
24 |
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"single_word": false,
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"special": true
|
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},
|
27 |
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"104": {
|
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"content": "[UNK]",
|
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+
"lstrip": false,
|
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"normalized": false,
|
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"rstrip": false,
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"single_word": false,
|
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"special": true
|
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},
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"30526": {
|
36 |
+
"content": "<mask>",
|
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"lstrip": true,
|
38 |
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"normalized": false,
|
39 |
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"rstrip": false,
|
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"single_word": false,
|
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"special": true
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}
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},
|
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"bos_token": "<s>",
|
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"clean_up_tokenization_spaces": true,
|
46 |
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"cls_token": "<s>",
|
47 |
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"do_basic_tokenize": true,
|
48 |
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"do_lower_case": true,
|
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"eos_token": "</s>",
|
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"mask_token": "<mask>",
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"max_length": 512,
|
52 |
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"model_max_length": 512,
|
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"never_split": null,
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54 |
+
"pad_to_multiple_of": null,
|
55 |
+
"pad_token": "<pad>",
|
56 |
+
"pad_token_type_id": 0,
|
57 |
+
"padding_side": "right",
|
58 |
+
"sep_token": "</s>",
|
59 |
+
"stride": 0,
|
60 |
+
"strip_accents": null,
|
61 |
+
"tokenize_chinese_chars": true,
|
62 |
+
"tokenizer_class": "MPNetTokenizer",
|
63 |
+
"truncation_side": "right",
|
64 |
+
"truncation_strategy": "longest_first",
|
65 |
+
"unk_token": "[UNK]"
|
66 |
+
}
|
vocab.txt
ADDED
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|
|