Push model using huggingface_hub.
Browse files- README.md +8 -52
- config.json +1 -1
- config_sentence_transformers.json +1 -1
- config_setfit.json +2 -2
- model_head.pkl +2 -2
README.md
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---
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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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- 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: What are some cool ways to describe a hidden civilization
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inference: true
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model-index:
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- name: SetFit
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results:
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type: text-classification
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name: Text Classification
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dataset:
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name: Unknown
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type: unknown
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split: test
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metrics:
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- type: accuracy
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value: 1.0
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name: Accuracy
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---
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# 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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| comments_assistance | <ul><li>'Can you identify specific areas that need improvement in my text'</li><li>'Point out the flaws in my writing style, please'</li><li>'Which parts of my draft are the weakest'</li></ul> |
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| chat_assistance | <ul><li>"How do I make my character's driving force more compelling"</li><li>"Any tips to deepen my protagonist's underlying goals"</li><li>"Suggestions for strengthening the reasons behind my character's actions"</li></ul> |
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| pro_subscription_assistance | <ul><li>'How does the Pro version elevate my writing experience'</li><li>'Could you list the premium perks of Quarkle Pro'</li><li>'What special advantages come with upgrading to Pro'</li></ul> |
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## Evaluation
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### Metrics
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| Label | Accuracy |
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| **all** | 1.0 |
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## Uses
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### Direct Use for Inference
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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("
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```
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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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| Word count | 1 | 8.9364 | 15 |
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| Label | Training Sample Count |
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| chat_assistance | 163 |
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| comments_assistance | 156 |
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| pro_subscription_assistance | 121 |
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### Framework Versions
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- Python: 3.10.15
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- SetFit: 1.
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- Sentence Transformers: 3.3.1
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- Transformers: 4.
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- PyTorch: 2.5.1
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- Datasets: 3.2.0
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- Tokenizers: 0.21.0
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---
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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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widget: []
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metrics:
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- accuracy
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pipeline_tag: text-classification
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library_name: setfit
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inference: true
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---
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# 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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# 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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## Training Details
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### Framework Versions
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- Python: 3.10.15
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- SetFit: 1.1.1
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- Sentence Transformers: 3.3.1
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- Transformers: 4.48.0
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- PyTorch: 2.5.1
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- Datasets: 3.2.0
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- Tokenizers: 0.21.0
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config.json
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.48.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "3.3.1",
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"transformers": "4.
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"pytorch": "2.5.1"
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"prompts": {},
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"__version__": {
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"sentence_transformers": "3.3.1",
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"transformers": "4.48.0",
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"pytorch": "2.5.1"
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"prompts": {},
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config_setfit.json
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"normalize_embeddings": true,
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"labels": [
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"chat_assistance",
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"comments_assistance",
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"pro_subscription_assistance"
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]
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}
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{
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"labels": [
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"chat_assistance",
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"comments_assistance",
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"pro_subscription_assistance"
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],
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"normalize_embeddings": true
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}
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model_head.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:2683d8e5c5262347956a652b78c20ade84f01edde6623f741173bfb873f6b399
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size 10399
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