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 |
positive |
- 'HELSINKI ( AFX ) - Nokian Tyres reported a fourth quarter pretax profit of 61.5 mln eur , up from 48.6 mln on the back of strong sales .'
- 'Equity ratio was 60.9 % compared to 54.2 % In the third quarter of 2007 , net sales of the Frozen Foods Business totaled EUR 11.0 , up by about 5 % from the third quarter of 2006 .'
- "`` After a long , unprofitable period the Food Division posted a profitable result , which speaks of a healthier cost structure and a new approach in business operations , '' Rihko said ."
|
neutral |
- 'Their names have not yet been released .'
- 'The contract includes design , construction , delivery of equipment , installation and commissioning .'
- "Tieto 's service is also used to send , process and receive materials related to absentee voting ."
|
negative |
- 'The company confirmed its estimate for lower revenue for the whole 2009 than the year-ago EUR93 .9 m as given in the interim report on 5 August 2009 .'
- 'Acando AB ( ACANB SS ) fell 8.9 percent to 13.35 kronor , the lowest close since Dec. 11 .'
- 'Okmetic expects its net sales for the first half of 2009 to be less than in 2008 .'
|
Evaluation
Metrics
Label |
Accuracy |
all |
0.9426 |
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("moshew/bge-small-en-v1.5-SetFit-FSA")
preds = model("The combined value of the planned investments is about EUR 30mn .")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
2 |
22.4020 |
60 |
Label |
Training Sample Count |
negative |
266 |
neutral |
1142 |
positive |
403 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- 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 |
0.0004 |
1 |
0.2832 |
- |
0.0221 |
50 |
0.209 |
- |
0.0442 |
100 |
0.1899 |
- |
0.0663 |
150 |
0.1399 |
- |
0.0883 |
200 |
0.1274 |
- |
0.1104 |
250 |
0.0586 |
- |
0.1325 |
300 |
0.0756 |
- |
0.1546 |
350 |
0.0777 |
- |
0.1767 |
400 |
0.0684 |
- |
0.1988 |
450 |
0.0311 |
- |
0.2208 |
500 |
0.0102 |
- |
0.2429 |
550 |
0.052 |
- |
0.2650 |
600 |
0.0149 |
- |
0.2871 |
650 |
0.1042 |
- |
0.3092 |
700 |
0.061 |
- |
0.3313 |
750 |
0.0083 |
- |
0.3534 |
800 |
0.0036 |
- |
0.3754 |
850 |
0.002 |
- |
0.3975 |
900 |
0.0598 |
- |
0.4196 |
950 |
0.0036 |
- |
0.4417 |
1000 |
0.0027 |
- |
0.4638 |
1050 |
0.0617 |
- |
0.4859 |
1100 |
0.0015 |
- |
0.5080 |
1150 |
0.0022 |
- |
0.5300 |
1200 |
0.0016 |
- |
0.5521 |
1250 |
0.0009 |
- |
0.5742 |
1300 |
0.0013 |
- |
0.5963 |
1350 |
0.0009 |
- |
0.6184 |
1400 |
0.0015 |
- |
0.6405 |
1450 |
0.0018 |
- |
0.6625 |
1500 |
0.0015 |
- |
0.6846 |
1550 |
0.0018 |
- |
0.7067 |
1600 |
0.0016 |
- |
0.7288 |
1650 |
0.0022 |
- |
0.7509 |
1700 |
0.0013 |
- |
0.7730 |
1750 |
0.0108 |
- |
0.7951 |
1800 |
0.0016 |
- |
0.8171 |
1850 |
0.0021 |
- |
0.8392 |
1900 |
0.002 |
- |
0.8613 |
1950 |
0.0015 |
- |
0.8834 |
2000 |
0.0016 |
- |
0.9055 |
2050 |
0.0028 |
- |
0.9276 |
2100 |
0.0013 |
- |
0.9496 |
2150 |
0.0019 |
- |
0.9717 |
2200 |
0.0075 |
- |
0.9938 |
2250 |
0.0015 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.5.1
- Transformers: 4.38.1
- PyTorch: 2.1.0+cu121
- 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}
}