SetFit with avsolatorio/GIST-small-Embedding-v0
This is a SetFit model that can be used for Text Classification. This SetFit model uses avsolatorio/GIST-small-Embedding-v0 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 |
subjective |
- 'Stakeholder capitalism poisons democracy and partisan politics poisons capitalism.'
- 'There is yet everywhere a deficit in the public revenue because the shrinkage in everything taxable was so sudden and violent.'
- 'Our system of unbridled profit-focused capitalism used to serve as perhaps the most important of those sanctuaries, but no longer.'
|
objective |
- 'But a top buying agent tells me that access to 13 can be gained if you know the right people.'
- 'A portion of positive tests around the country is being forwarded to the agency for genetic sequencing, according to a report by CBS News.'
- 'asked American Federation of Teachers President Randi Weingarten.'
|
Evaluation
Metrics
Label |
Accuracy |
all |
0.8446 |
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("setfit_model_id")
preds = model("As the total national income falls, the proportion of it absorbed by government will rise.")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
1 |
22.9219 |
77 |
Label |
Training Sample Count |
objective |
128 |
subjective |
128 |
Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- 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 |
0.0010 |
1 |
0.2715 |
- |
0.0484 |
50 |
0.2469 |
- |
0.0969 |
100 |
0.2247 |
- |
0.1453 |
150 |
0.0501 |
- |
0.1938 |
200 |
0.0039 |
- |
0.2422 |
250 |
0.0014 |
- |
0.2907 |
300 |
0.0011 |
- |
0.3391 |
350 |
0.0014 |
- |
0.3876 |
400 |
0.001 |
- |
0.4360 |
450 |
0.0009 |
- |
0.4845 |
500 |
0.0008 |
- |
0.5329 |
550 |
0.0008 |
- |
0.5814 |
600 |
0.0008 |
- |
0.6298 |
650 |
0.0007 |
- |
0.6783 |
700 |
0.0007 |
- |
0.7267 |
750 |
0.0006 |
- |
0.7752 |
800 |
0.0007 |
- |
0.8236 |
850 |
0.0006 |
- |
0.8721 |
900 |
0.0005 |
- |
0.9205 |
950 |
0.0007 |
- |
0.9690 |
1000 |
0.0007 |
- |
Framework Versions
- Python: 3.11.9
- SetFit: 1.0.3
- Sentence Transformers: 3.0.0
- Transformers: 4.40.2
- PyTorch: 2.1.2
- Datasets: 2.19.1
- Tokenizers: 0.19.1
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}
}