Text Classification
setfit
Safetensors
sentence-transformers
distilbert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use wikd/test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use wikd/test_model with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("wikd/test_model") - sentence-transformers
How to use wikd/test_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("wikd/test_model") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Add SetFit model
Browse files- README.md +12 -10
- config_setfit.json +2 -2
- model.safetensors +1 -1
- model_head.pkl +1 -1
README.md
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@@ -62,10 +62,10 @@ The model has been trained using an efficient few-shot learning technique that i
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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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## Evaluation
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### Training Set Metrics
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| Training set | Min | Median | Max |
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| Word count | 4 |
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| Label | Training Sample Count |
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### Training Hyperparameters
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- batch_size: (16, 16)
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- load_best_model_at_end: False
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### Training Results
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| Epoch
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### Framework Versions
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- Python: 3.11.8
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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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| 0 | <ul><li>'I need to speak to a real person, not a dumb machine.'</li><li>'Stop with the automated nonsense and connect me to a human!'</li><li>'Your automated system is beyond frustrating, let me talk to someone!'</li></ul> |
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| 1 | <ul><li>'I love your new product!'</li><li>'The delivery was very quick!'</li><li>'I would recommend this company to a friend'</li></ul> |
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## Evaluation
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### Training Set Metrics
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| Training set | Min | Median | Max |
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| Word count | 4 | 10.0 | 14 |
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| Label | Training Sample Count |
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### Training Hyperparameters
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- batch_size: (16, 16)
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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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| 0.0077 | 1 | 0.1479 | - |
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| 0.3846 | 50 | 0.0008 | - |
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| 0.7692 | 100 | 0.0005 | - |
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### Framework Versions
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- Python: 3.11.8
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config_setfit.json
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"normalize_embeddings": false
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}
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model.safetensors
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model_head.pkl
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