đ SetFit with sentence-transformers/all-MiniLM-L6-v2
This model is a SetFit-based solution that leverages the sentence-transformers/all-MiniLM-L6-v2
model. It's designed for text classification tasks, offering high accuracy with efficient few-shot learning.
đ Quick Start
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-MiniLM-L6-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
đ Documentation
Model Details
Model Information
Model Sources
Model Labels
Label |
Examples |
governing laws |
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counterparts |
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notices |
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entire agreements |
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severability |
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- 'Wherever possible, each provision of this Agreement shall be interpreted in such manner as to be effective and valid under applicable law, but if any provision of this Agreement shall be prohibited by or inva
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đ§ Technical Details
The model achieved an accuracy of 0.9425 on the test split of an unknown dataset.
task |
dataset |
metrics |
type: text-classification name: Text Classification |
name: Unknown type: unknown split: test |
type: accuracy value: 0.9425 name: Accuracy |