SetFit with BAAI/bge-small-en-v1.5
This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 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 |
1 |
- 'a sensitive , modest comic tragedy that works as both character study and symbolic examination of the huge economic changes sweeping modern china .'
- 'the year 2002 has conjured up more coming-of-age stories than seem possible , but take care of my cat emerges as the very best of them .'
- 'amy and matthew have a bit of a phony relationship , but the film works in spite of it .'
|
0 |
- 'works on the whodunit level as its larger themes get lost in the murk of its own making'
- "one of those strained caper movies that 's hardly any fun to watch and begins to vaporize from your memory minutes after it ends ."
- "shunji iwai 's all about lily chou chou is a beautifully shot , but ultimately flawed film about growing up in japan ."
|
Evaluation
Metrics
Label |
Accuracy |
all |
0.8622 |
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("Jorgeutd/setfit-bge-small-v1.5-sst2-50-shot")
preds = model("it 's a bad sign in a thriller when you instantly know whodunit .")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
3 |
21.31 |
50 |
Label |
Training Sample Count |
0 |
50 |
1 |
50 |
Training Hyperparameters
- batch_size: (16, 16)
- 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 |
0.0031 |
1 |
0.2515 |
- |
0.1567 |
50 |
0.2298 |
- |
0.3135 |
100 |
0.2134 |
- |
0.4702 |
150 |
0.0153 |
- |
0.6270 |
200 |
0.0048 |
- |
0.7837 |
250 |
0.0024 |
- |
0.9404 |
300 |
0.0023 |
- |
1.0972 |
350 |
0.0016 |
- |
1.2539 |
400 |
0.0016 |
- |
1.4107 |
450 |
0.001 |
- |
1.5674 |
500 |
0.0013 |
- |
1.7241 |
550 |
0.0008 |
- |
1.8809 |
600 |
0.0008 |
- |
2.0376 |
650 |
0.0007 |
- |
2.1944 |
700 |
0.0008 |
- |
2.3511 |
750 |
0.0008 |
- |
2.5078 |
800 |
0.0007 |
- |
2.6646 |
850 |
0.0006 |
- |
2.8213 |
900 |
0.0006 |
- |
2.9781 |
950 |
0.0005 |
- |
3.1348 |
1000 |
0.0006 |
- |
3.2915 |
1050 |
0.0006 |
- |
3.4483 |
1100 |
0.0005 |
- |
3.6050 |
1150 |
0.0005 |
- |
3.7618 |
1200 |
0.0005 |
- |
3.9185 |
1250 |
0.0005 |
- |
4.0752 |
1300 |
0.0005 |
- |
4.2320 |
1350 |
0.0004 |
- |
4.3887 |
1400 |
0.0004 |
- |
4.5455 |
1450 |
0.0004 |
- |
4.7022 |
1500 |
0.0003 |
- |
4.8589 |
1550 |
0.0006 |
- |
5.0157 |
1600 |
0.0007 |
- |
5.1724 |
1650 |
0.0004 |
- |
5.3292 |
1700 |
0.0004 |
- |
5.4859 |
1750 |
0.0004 |
- |
5.6426 |
1800 |
0.0004 |
- |
5.7994 |
1850 |
0.0003 |
- |
5.9561 |
1900 |
0.0004 |
- |
6.1129 |
1950 |
0.0003 |
- |
6.2696 |
2000 |
0.0003 |
- |
6.4263 |
2050 |
0.0005 |
- |
6.5831 |
2100 |
0.0003 |
- |
6.7398 |
2150 |
0.0003 |
- |
6.8966 |
2200 |
0.0003 |
- |
7.0533 |
2250 |
0.0003 |
- |
7.2100 |
2300 |
0.0003 |
- |
7.3668 |
2350 |
0.0003 |
- |
7.5235 |
2400 |
0.0002 |
- |
7.6803 |
2450 |
0.0003 |
- |
7.8370 |
2500 |
0.0003 |
- |
7.9937 |
2550 |
0.0003 |
- |
8.1505 |
2600 |
0.0003 |
- |
8.3072 |
2650 |
0.0003 |
- |
8.4639 |
2700 |
0.0003 |
- |
8.6207 |
2750 |
0.0003 |
- |
8.7774 |
2800 |
0.0004 |
- |
8.9342 |
2850 |
0.0002 |
- |
9.0909 |
2900 |
0.0003 |
- |
9.2476 |
2950 |
0.0004 |
- |
9.4044 |
3000 |
0.0004 |
- |
9.5611 |
3050 |
0.0003 |
- |
9.7179 |
3100 |
0.0004 |
- |
9.8746 |
3150 |
0.0003 |
- |
Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.39.1
- PyTorch: 2.1.0
- Datasets: 2.18.0
- Tokenizers: 0.15.2
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}
}