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 |
| 3 |
- 'an indispensable peek at the art and the agony of making people laugh .'
- "there 's a lot to recommend read my lips ."
- 'but it also has many of the things that made the first one charming .'
|
| 1 |
- 'a baffling mixed platter of gritty realism and magic realism with a hard-to-swallow premise .'
- 'each scene drags , underscoring the obvious , and sentiment is slathered on top .'
- 'even bigger and more ambitious than the first installment , spy kids 2 looks as if it were made by a highly gifted 12-year-old instead of a grown man .'
|
| 4 |
- 'about schmidt is undoubtedly one of the finest films of the year .'
- 'a compelling pre-wwii drama with vivid characters and a warm , moving message .'
- 'twenty years later , e.t. is still a cinematic touchstone .'
|
| 2 |
- 'an unremarkable , modern action\/comedy buddy movie whose only nod to nostalgia is in the title .'
- 'a movie that seems motivated more by a desire to match mortarboards with dead poets society and good will hunting than by its own story .'
- "i ca n't ."
|
| 0 |
- '... about as exciting to watch as two last-place basketball teams playing one another on the final day of the season .'
- '... no charm , no laughs , no fun , no reason to watch .'
- 'this one aims for the toilet and scores a direct hit .'
|
Evaluation
Metrics
| Label |
Accuracy |
| all |
0.4163 |
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("vidhi0206/setfit-paraphrase-mpnet-sst5")
preds = model("my response to the film is best described as lukewarm .")
Training Details
Training Set Metrics
| Training set |
Min |
Median |
Max |
| Word count |
4 |
16.2 |
35 |
| Label |
Training Sample Count |
| 0 |
8 |
| 1 |
8 |
| 2 |
8 |
| 3 |
8 |
| 4 |
8 |
Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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 |
| 0.005 |
1 |
0.2435 |
- |
| 0.25 |
50 |
0.1137 |
- |
| 0.5 |
100 |
0.0018 |
- |
| 0.75 |
150 |
0.0049 |
- |
| 1.0 |
200 |
0.0026 |
- |
Framework Versions
- Python: 3.8.10
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.37.2
- PyTorch: 2.2.0+cu121
- Datasets: 2.17.0
- Tokenizers: 0.15.1
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}
}
