SetFit with intfloat/multilingual-e5-small
This is a SetFit model that can be used for Text Classification. This SetFit model uses intfloat/multilingual-e5-small 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.
Model Details
Model Description
Model Sources
Model Labels
Label |
Examples |
independent |
- 'Comment rédiger un contrat de travail ?'
- 'Quels sont les impôts et taxes applicables aux entreprises ?'
- 'Comment peut-on contester un licenciement abusif ?'
|
follow_up |
- 'Quelles sont les conséquences de cette loi ?'
- "Comment cette loi s'inscrit-elle dans le cadre plus large du droit algérien ?"
- "Comment puis-je obtenir plus d'informations sur ce sujet ?"
|
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
model = SetFitModel.from_pretrained("super-cinnamon/fewshot-followup-multi-e5")
preds = model("Comment se déroule une procédure de divorce ?")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
1 |
9.6184 |
16 |
Label |
Training Sample Count |
independent |
43 |
follow_up |
33 |
Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (10, 10)
- max_steps: -1
- sampling_strategy: oversampling
- 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 |
0.0027 |
1 |
0.3915 |
- |
0.1326 |
50 |
0.3193 |
- |
0.2653 |
100 |
0.2252 |
- |
0.3979 |
150 |
0.1141 |
- |
0.5305 |
200 |
0.0197 |
- |
0.6631 |
250 |
0.0019 |
- |
0.7958 |
300 |
0.0021 |
- |
0.9284 |
350 |
0.0002 |
- |
1.0610 |
400 |
0.0008 |
- |
1.1936 |
450 |
0.0005 |
- |
1.3263 |
500 |
0.0002 |
- |
1.4589 |
550 |
0.0002 |
- |
1.5915 |
600 |
0.0007 |
- |
1.7241 |
650 |
0.0001 |
- |
1.8568 |
700 |
0.0003 |
- |
1.9894 |
750 |
0.0002 |
- |
2.1220 |
800 |
0.0001 |
- |
2.2546 |
850 |
0.0002 |
- |
2.3873 |
900 |
0.0 |
- |
2.5199 |
950 |
0.0003 |
- |
2.6525 |
1000 |
0.0001 |
- |
2.7851 |
1050 |
0.0001 |
- |
2.9178 |
1100 |
0.0001 |
- |
3.0504 |
1150 |
0.0001 |
- |
3.1830 |
1200 |
0.0001 |
- |
3.3156 |
1250 |
0.0001 |
- |
3.4483 |
1300 |
0.0001 |
- |
3.5809 |
1350 |
0.0001 |
- |
3.7135 |
1400 |
0.0 |
- |
3.8462 |
1450 |
0.0 |
- |
3.9788 |
1500 |
0.0 |
- |
4.1114 |
1550 |
0.0 |
- |
4.2440 |
1600 |
0.0001 |
- |
4.3767 |
1650 |
0.0001 |
- |
4.5093 |
1700 |
0.0001 |
- |
4.6419 |
1750 |
0.0001 |
- |
4.7745 |
1800 |
0.0 |
- |
4.9072 |
1850 |
0.0001 |
- |
5.0398 |
1900 |
0.0 |
- |
5.1724 |
1950 |
0.0001 |
- |
5.3050 |
2000 |
0.0 |
- |
5.4377 |
2050 |
0.0001 |
- |
5.5703 |
2100 |
0.0 |
- |
5.7029 |
2150 |
0.0 |
- |
5.8355 |
2200 |
0.0 |
- |
5.9682 |
2250 |
0.0001 |
- |
6.1008 |
2300 |
0.0001 |
- |
6.2334 |
2350 |
0.0 |
- |
6.3660 |
2400 |
0.0001 |
- |
6.4987 |
2450 |
0.0 |
- |
6.6313 |
2500 |
0.0 |
- |
6.7639 |
2550 |
0.0 |
- |
6.8966 |
2600 |
0.0 |
- |
7.0292 |
2650 |
0.0 |
- |
7.1618 |
2700 |
0.0 |
- |
7.2944 |
2750 |
0.0 |
- |
7.4271 |
2800 |
0.0001 |
- |
7.5597 |
2850 |
0.0 |
- |
7.6923 |
2900 |
0.0 |
- |
7.8249 |
2950 |
0.0 |
- |
7.9576 |
3000 |
0.0 |
- |
8.0902 |
3050 |
0.0 |
- |
8.2228 |
3100 |
0.0 |
- |
8.3554 |
3150 |
0.0 |
- |
8.4881 |
3200 |
0.0001 |
- |
8.6207 |
3250 |
0.0 |
- |
8.7533 |
3300 |
0.0 |
- |
8.8859 |
3350 |
0.0 |
- |
9.0186 |
3400 |
0.0001 |
- |
9.1512 |
3450 |
0.0 |
- |
9.2838 |
3500 |
0.0 |
- |
9.4164 |
3550 |
0.0001 |
- |
9.5491 |
3600 |
0.0 |
- |
9.6817 |
3650 |
0.0001 |
- |
9.8143 |
3700 |
0.0 |
- |
9.9469 |
3750 |
0.0001 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu118
- Datasets: 2.15.0
- Tokenizers: 0.15.0
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}
}