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 SetFitHead 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 |
- 'I consider that is more convenient to drive a car because you carry on more things in your own car than travelling by car .'
- 'In the last few years forensic biology has developed many aspects like better sensibility , robustness of results and less time required for analyze a sample , but what struck me most is how fast this change happens .'
- "The car is n't the best way for for the transport , because it produce much pollution , however the public transport is better to do a journey ."
|
6 |
- 'On the one hand travel by car are really much more convenient as give the chance to you to be independent .'
- 'When most people think about an important historical place in Italy , they think of Duomo , in Milano .'
- 'I like personality with childlike , so I like children .'
|
5 |
- 'Yours sincerely ,'
- 'This practice is considered those activities that anyone can do without any kind of special preparation .'
- 'Secondly , the public vehicle route are more far than usual route .'
|
7 |
- 'This conclusion become more prominent if we look into the data of the car companies and exponential growth in their sales figure and with low budget private cars in picture , scenario ddrastically changed in past 10 years'
- 'Recently I saw the thriller of mokingjay part 2 .'
- "An example of that is the marriage of homosexual where some state admit this marriage , others do n't ."
|
3 |
- 'After that , the sports day began formally .'
- 'In those years I lived the worst moments in my life .'
- 'On the one hand , in my country there are a lot of place to travel .'
|
2 |
- "Sharing houses or rooms have many advantages such as , cheap , safe , close to the university , and learn how to share everything with others . saving money and time will be more Obvious in university dormitories because monthly payments will be less than four times than hiring an apartment , and because it will be closer to the university , saving money and time is more efficient by reducing transportation 's costs"
- 'So , finally I suggest that it would be a great idea to combine the different types of activities , both popular and the newest .'
- 'Wszysycy residents of my village , they try to , so that our village was clear that pollute the environment as little as possible .'
|
4 |
- 'During summer I love to go to the beach and having sunbathing with my friends other than getting fun with them playing volleyball or run inside the water of the sea !'
- 'Jose is the best song . he is singing and talking in the party .'
- "She fell sleep again , didn't she ?"
|
0 |
- 'I work for the same large company for 25 years , now is the time to change and find new job opportunities .'
- 'A problem which was caused by us , human beings , with their target of making money without thinking of the effects .'
- 'He was waiting 2 hours for her .'
|
Evaluation
Metrics
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("HelgeKn/BEA2019-multi-class-20")
preds = model("Dear sir Dimara .")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
3 |
22.0 |
82 |
Label |
Training Sample Count |
0 |
20 |
1 |
20 |
2 |
20 |
3 |
20 |
4 |
20 |
5 |
20 |
6 |
20 |
7 |
20 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- 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 |
0.0025 |
1 |
0.3724 |
- |
0.125 |
50 |
0.2732 |
- |
0.25 |
100 |
0.3001 |
- |
0.375 |
150 |
0.2525 |
- |
0.5 |
200 |
0.1934 |
- |
0.625 |
250 |
0.1164 |
- |
0.75 |
300 |
0.0874 |
- |
0.875 |
350 |
0.0624 |
- |
1.0 |
400 |
0.052 |
- |
1.125 |
450 |
0.0569 |
- |
1.25 |
500 |
0.0248 |
- |
1.375 |
550 |
0.0071 |
- |
1.5 |
600 |
0.0124 |
- |
1.625 |
650 |
0.0087 |
- |
1.75 |
700 |
0.0086 |
- |
1.875 |
750 |
0.066 |
- |
2.0 |
800 |
0.0194 |
- |
Framework Versions
- Python: 3.9.13
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.36.0
- PyTorch: 2.1.1+cpu
- Datasets: 2.15.0
- 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}
}