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 |
| sadness |
- 'i am from new jersey and this first drink was consumed at a post prom party so i feel it s appropriately lame'
- 'i am the one feeling punished'
- 'i wouldn t feel submissive which has it s place but not in the work environment'
|
| love |
- 'i would rather take my chances on keeping my heart and getting it broken again and again then to stop feeling to stop caring to be bitter cross cynical'
- 'i still love to run and plan to keep it up but i don t want to once again register for so many races that i feel like every exercise moment needs to be devoted to running'
- 'i suddenly feel that this is more than a sweet love song that every girls could sing in front of their boyfriends'
|
| surprise |
- 'i was feeling an act of god at work in my life and it was an amazing feeling'
- 'i tween sat for my moms boss year old and year old boys this weekend id say babysit but that feels weird considering there were n'
- 'i started feeling funny and then friday i woke up sick as a dog'
|
| anger |
- 'i could of course go on with it feeling resentful of him with him being blissfully unaware of anything being wrong'
- 'i feel tortured because i am not allowed to enjoy food the way my friend can'
- 'i feel like i should be offended but yawwwn'
|
| joy |
- 'i was feeling over eager and hopped on to the tube to ride the eye of london'
- 'i am not feeling particularly creative'
- 'i woke on saturday feeling a little brighter and was very keen to get outdoors after spending all day friday wallowing in self pity'
|
| fear |
- 'im feeling pretty shaken at the moment'
- 'i know he is totally trainable and can be free of his arm chewing habits i feel that the kids would be too nervous around him during the training process'
- 'i am feeling pretty restless right now while typing this'
|
Evaluation
Metrics
| Label |
Accuracy |
| all |
0.4584 |
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("dendimaki/apeiron-v4")
preds = model("i feel for you despite the bitterness and longing")
Training Details
Training Set Metrics
| Training set |
Min |
Median |
Max |
| Word count |
4 |
17.6458 |
55 |
| Label |
Training Sample Count |
| sadness |
8 |
| joy |
8 |
| love |
8 |
| anger |
8 |
| fear |
8 |
| surprise |
8 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- 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: True
Training Results
| Epoch |
Step |
Training Loss |
Validation Loss |
| 0.0083 |
1 |
0.2802 |
- |
| 0.4167 |
50 |
0.1302 |
- |
| 0.8333 |
100 |
0.0121 |
- |
| 1.0 |
120 |
- |
0.2668 |
| 1.25 |
150 |
0.003 |
- |
| 1.6667 |
200 |
0.0007 |
- |
| 2.0 |
240 |
- |
0.2562 |
| 2.0833 |
250 |
0.0008 |
- |
| 2.5 |
300 |
0.0009 |
- |
| 2.9167 |
350 |
0.0007 |
- |
| 3.0 |
360 |
- |
0.2572 |
| 3.3333 |
400 |
0.0005 |
- |
| 3.75 |
450 |
0.0005 |
- |
| 4.0 |
480 |
- |
0.2571 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.0
- 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}
}
