SetFit with BAAI/bge-small-en-v1.5
This is a SetFit model trained on the SetFit/sst2 dataset that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 as the Sentence Transformer embedding model. A SetFitHead 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.
Model Details
Model Description
Model Sources
Model Labels
| Label | Examples |
|---|
| 1 | 'a stirring , funny and finally transporting re-imagining of beauty and the beast and 1930s horror films''this is a visually stunning rumination on love , memory , history and the war between art and commerce .'"jonathan parker 's bartleby should have been the be-all-end-all of the modern-office anomie films ." |
| 0 | 'apparently reassembled from the cutting-room floor of any given daytime soap .'"they presume their audience wo n't sit still for a sociology lesson , however entertainingly presented , so they trot out the conventional science-fiction elements of bug-eyed monsters and futuristic women in skimpy clothes ."'a fan film that for the uninitiated plays better on video with the sound turned down .' |
Evaluation
Metrics
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("dkorat/bge-small-en-v1.5_setfit-sst2-english")
# Run inference
preds = model("a noble failure .")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|
| Word count | 2 | 19.591 | 46 |
| Label | Training Sample Count |
|---|
| 0 | 479 |
| 1 | 521 |
Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 1
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|
| 0.008 | 1 | 0.241 | - |
| 0.4 | 50 | 0.2525 | - |
| 0.8 | 100 | 0.0607 | - |
Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.3.0
- Transformers: 4.37.2
- PyTorch: 2.1.2+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.1
Citation
BibTeX
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}