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 |
| 1 |
- 'i don t feel so self assured i need to compete or to justify why i m so clearly not doing as well as someone else'
- 'i should do but i think it means that i should always be open to opportunities of inviting and involving others in ministries and that i should be creative in finding ways for others to participate in and feel welcomed into such ministries'
- 'i feel like im going to be way more successful a writer because of it'
|
| 4 |
- 'i feel so weird and scattered with all wonders about a million different things'
- 'i mean already as a parent from the moment the iolani left my body i can tell you i feel like im constantly fearful for something horrible happening to her thats out of my control'
- 'i think i was feeling vulnerable due to the stress of having to buy a new sewing machine and printer'
|
| 5 |
- 'i feel like this inside theres one thing i wanna know whats so funny bout peace love and understanding'
- 'i feel like itd be strange at the least and possibly offensive to tell a gay friend id like to experiment or something like that'
- 'i am not sure why in that moment that i thought i would be able to feel it hellip but it was pretty funny'
|
| 2 |
- 'i can feel that gentle rhythm imprinted on my skin i vibrates up my arm my stomach clenches my legs squeeze i forget his own leg has somehow ended up between mine'
- 'i feel specially fond of'
- 'i just feel like i dont like supporting walmart because maceys has such good family values and is closed on sundays and isnt trying to take over mom and pop stores but i have to be a smart consumer too'
|
| 3 |
- 'i am sure the vast majority of decent working class people feel insulted about being derided as unable to be respectful towards referees and are the parents who watch their child s match shouting abuse and swearing etc'
- 'im feeling irritated by her friggin name'
- 'i feel heartless now feeling bored and not believe in love anymore'
|
| 0 |
- 'i had just begun to feel like teaching was my metier but am now resigned to the fact that i likely wont teach at university ever again'
- 'i think the most common one that everyone has experienced is that doom and gloom feeling where you just feel like something tragic just happened'
- 'i feel a bit foolish now because in the last years they havent come back to my home town and i have had to travel to england to see them'
|
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("vidhi0206/setfit-paraphrase-mpnet-emotionv")
preds = model("i am feeling very indecisive and spontaneous")
Training Details
Training Set Metrics
| Training set |
Min |
Median |
Max |
| Word count |
5 |
20.4375 |
47 |
| Label |
Training Sample Count |
| 0 |
8 |
| 1 |
8 |
| 2 |
8 |
| 3 |
8 |
| 4 |
8 |
| 5 |
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 |
| 0.0042 |
1 |
0.2804 |
- |
| 0.2083 |
50 |
0.0724 |
- |
| 0.4167 |
100 |
0.0512 |
- |
| 0.625 |
150 |
0.0108 |
- |
| 0.8333 |
200 |
0.0027 |
- |
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}
}
