SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 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 |
| 4 |
- 'The Super Mario Bros. Movie Expected To Pass $1 Billion, Biggest Movie Release This Year - Kotaku'
- 'Richard Lewis Has Parkinson’s Disease, Finished With Stand-Up Comedy Career - Deadline'
- "EXCLUSIVE Dame Mary Quant's plans for 'small funeral' near her home - Daily Mail"
|
| 3 |
- 'GPT-5 not in the works currently: OpenAI CEO Sam Altman - The Economic Times'
- 'The 2023 Am Law 100: Ranked by Gross Revenue
|
| 5 |
- "I used all 2023 flagships — here's why the Galaxy S23 Ultra is my favorite phone - Android Central"
- "Google's AI experts on the future of artificial intelligence
|
| 0 |
- 'Fernando Tatis Jr. to make Padres return - MLB.com'
- 'Knicks-Cavaliers Game 3 live updates: Score, news, more from NBA Playoffs - New York Post '
- 'Josh Donaldson Likely To Miss Multiple Weeks With Hamstring Strain - MLB Trade Rumors'
|
| 2 |
- 'Are Fermented Foods Actually Good for You? - Lifehacker'
- 'ADHD medication
|
| 1 |
- 'Creating Artificial Avians: A Novel Neural Network Generates Realistic Bird Pictures from Text using Common Sense - Neuroscience News'
- 'Consciousness begins with feeling, not thinking
|
Evaluation
Metrics
| Label |
Accuracy |
| all |
0.8577 |
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("Kevinger/setfit-newsapi")
preds = model("GIANT 130-foot asteroid rushing towards Earth TODAY at 42404 kmph, NASA warns - HT Tech")
Training Details
Training Set Metrics
| Training set |
Min |
Median |
Max |
| Word count |
4 |
9.1771 |
22 |
| Label |
Training Sample Count |
| 0 |
16 |
| 1 |
16 |
| 2 |
16 |
| 3 |
16 |
| 4 |
16 |
| 5 |
16 |
Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- 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 |
| 0.0021 |
1 |
0.2926 |
- |
| 0.1042 |
50 |
0.0446 |
- |
| 0.2083 |
100 |
0.0023 |
- |
| 0.3125 |
150 |
0.0011 |
- |
| 0.4167 |
200 |
0.001 |
- |
| 0.5208 |
250 |
0.0007 |
- |
| 0.625 |
300 |
0.0007 |
- |
| 0.7292 |
350 |
0.0009 |
- |
| 0.8333 |
400 |
0.0075 |
- |
| 0.9375 |
450 |
0.0006 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- 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}
}
