Davide1999
commited on
Commit
•
231944f
1
Parent(s):
c8f5383
Add SetFit ABSA model
Browse files- 1_Pooling/config.json +10 -0
- README.md +219 -0
- config.json +24 -0
- config_sentence_transformers.json +10 -0
- config_setfit.json +9 -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 +59 -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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"include_prompt": true
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}
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README.md
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---
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base_model: sentence-transformers/paraphrase-mpnet-base-v2
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library_name: setfit
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metrics:
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- accuracy
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pipeline_tag: text-classification
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tags:
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- setfit
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- absa
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- sentence-transformers
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- text-classification
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- generated_from_setfit_trainer
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widget:
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- text: bar:After really enjoying ourselves at the bar we sat down at a table and
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had dinner.
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- text: interior decor:this little place has a cute interior decor and affordable
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city prices.
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- text: cuisine:The cuisine from what I've gathered is authentic Taiwanese, though
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its very different from what I've been accustomed to in Taipei.
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- text: dining:Go here for a romantic dinner but not for an all out wow dining experience.
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- text: Taipei:The cuisine from what I've gathered is authentic Taiwanese, though
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its very different from what I've been accustomed to in Taipei.
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inference: false
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---
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# SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). 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. In particular, this model is in charge of filtering aspect span candidates.
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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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This model was trained within the context of a larger system for ABSA, which looks like so:
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1. Use a spaCy model to select possible aspect span candidates.
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2. **Use this SetFit model to filter these possible aspect span candidates.**
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3. Use a SetFit model to classify the filtered aspect span candidates.
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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 body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
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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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- **spaCy Model:** en_core_web_lg
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- **SetFitABSA Aspect Model:** [Davide1999/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect](https://huggingface.co/Davide1999/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect)
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- **SetFitABSA Polarity Model:** [Davide1999/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity](https://huggingface.co/Davide1999/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity)
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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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### Model Labels
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| Label | Examples |
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|:----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| aspect | <ul><li>'staff:But the staff was so horrible to us.'</li><li>"food:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"food:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."</li></ul> |
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| no aspect | <ul><li>"factor:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"deficiencies:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"Teodora:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li></ul> |
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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 AbsaModel
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# Download from the 🤗 Hub
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model = AbsaModel.from_pretrained(
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"Davide1999/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect",
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"Davide1999/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity",
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)
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# Run inference
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preds = model("The food was great, but the venue is just way too busy.")
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```
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<!--
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### Downstream Use
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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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### 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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## Bias, Risks and Limitations
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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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### 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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## Training Details
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### Training Set Metrics
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| Training set | Min | Median | Max |
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|:-------------|:----|:--------|:----|
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| Word count | 4 | 17.9296 | 37 |
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| Label | Training Sample Count |
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|:----------|:----------------------|
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| no aspect | 71 |
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| aspect | 128 |
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### Training Hyperparameters
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- batch_size: (16, 2)
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- num_epochs: (1, 16)
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- max_steps: -1
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- sampling_strategy: oversampling
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- body_learning_rate: (2e-05, 1e-05)
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- head_learning_rate: 0.01
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- loss: CosineSimilarityLoss
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- distance_metric: cosine_distance
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- margin: 0.25
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- end_to_end: False
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- use_amp: False
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- warmup_proportion: 0.1
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- seed: 42
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- eval_max_steps: -1
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- load_best_model_at_end: False
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:------:|:----:|:-------------:|:---------------:|
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| 0.0007 | 1 | 0.3344 | - |
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| 0.0370 | 50 | 0.2609 | - |
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| 0.0740 | 100 | 0.1838 | - |
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| 0.1109 | 150 | 0.0581 | - |
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| 0.1479 | 200 | 0.0028 | - |
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| 0.1849 | 250 | 0.0005 | - |
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| 0.2219 | 300 | 0.0005 | - |
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| 0.2589 | 350 | 0.0005 | - |
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| 0.2959 | 400 | 0.0002 | - |
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| 0.3328 | 450 | 0.0003 | - |
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| 0.3698 | 500 | 0.0001 | - |
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| 0.4068 | 550 | 0.0001 | - |
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| 0.4438 | 600 | 0.0001 | - |
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| 0.4808 | 650 | 0.0001 | - |
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| 0.5178 | 700 | 0.0001 | - |
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| 0.5547 | 750 | 0.0001 | - |
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| 0.5917 | 800 | 0.0001 | - |
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| 0.6287 | 850 | 0.0002 | - |
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| 0.6657 | 900 | 0.0001 | - |
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| 0.7027 | 950 | 0.0001 | - |
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| 0.7396 | 1000 | 0.0001 | - |
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| 0.7766 | 1050 | 0.0 | - |
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| 0.8136 | 1100 | 0.0001 | - |
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| 0.8506 | 1150 | 0.0001 | - |
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| 0.8876 | 1200 | 0.0001 | - |
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| 0.9246 | 1250 | 0.0001 | - |
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| 0.9615 | 1300 | 0.0001 | - |
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| 0.9985 | 1350 | 0.0001 | - |
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### Framework Versions
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- Python: 3.10.12
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- SetFit: 1.0.3
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- Sentence Transformers: 3.0.1
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- spaCy: 3.7.5
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- Transformers: 4.39.0
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- PyTorch: 2.3.0+cu121
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- Datasets: 2.20.0
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- Tokenizers: 0.15.2
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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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*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": "sentence-transformers/paraphrase-mpnet-base-v2",
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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": "4.39.0",
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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": "3.0.1",
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"transformers": "4.39.0",
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"pytorch": "2.3.0+cu121"
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},
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"prompts": {},
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"default_prompt_name": null,
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"similarity_fn_name": null
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}
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config_setfit.json
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{
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"span_context": 0,
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"labels": [
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"no aspect",
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"aspect"
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],
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"normalize_embeddings": false,
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"spacy_model": "en_core_web_lg"
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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:af1aff0cb8d819c7fa3951b367b794625dd98e91d77b94a6bd9ba83cbbbad97d
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size 437967672
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model_head.pkl
ADDED
@@ -0,0 +1,3 @@
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1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:49ae4b4d33508540c8161404926d387e9a182d6c7cfb86e0ec6ae0264a65eef3
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size 6991
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modules.json
ADDED
@@ -0,0 +1,14 @@
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1 |
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[
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2 |
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{
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3 |
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"idx": 0,
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4 |
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"name": "0",
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5 |
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"path": "",
|
6 |
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"type": "sentence_transformers.models.Transformer"
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7 |
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},
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{
|
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"idx": 1,
|
10 |
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"name": "1",
|
11 |
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"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
}
|
14 |
+
]
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sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
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|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
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special_tokens_map.json
ADDED
@@ -0,0 +1,51 @@
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1 |
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{
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"bos_token": {
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3 |
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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
|
50 |
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}
|
51 |
+
}
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tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
ADDED
@@ -0,0 +1,59 @@
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{
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"added_tokens_decoder": {
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3 |
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"0": {
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4 |
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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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"special": true
|
10 |
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},
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"1": {
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12 |
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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
|
18 |
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},
|
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"2": {
|
20 |
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"content": "</s>",
|
21 |
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
|
24 |
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"single_word": false,
|
25 |
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"special": true
|
26 |
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},
|
27 |
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"104": {
|
28 |
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"content": "[UNK]",
|
29 |
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"lstrip": false,
|
30 |
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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
|
34 |
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},
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"30526": {
|
36 |
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"content": "<mask>",
|
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"lstrip": true,
|
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"normalized": false,
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39 |
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"rstrip": false,
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"single_word": false,
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"special": true
|
42 |
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}
|
43 |
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},
|
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"bos_token": "<s>",
|
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"clean_up_tokenization_spaces": true,
|
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"cls_token": "<s>",
|
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"do_basic_tokenize": true,
|
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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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"model_max_length": 512,
|
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"never_split": null,
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"pad_token": "<pad>",
|
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"sep_token": "</s>",
|
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"strip_accents": null,
|
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"tokenize_chinese_chars": true,
|
57 |
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"tokenizer_class": "MPNetTokenizer",
|
58 |
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"unk_token": "[UNK]"
|
59 |
+
}
|
vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
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