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 |
6 |
- 'The man claps his hands together. The man'
- 'Emerging in open water, he does a breaststroke toward the murky. He'
- 'The girl does 2 perfect flips. The girls'
|
3 |
- 'The younger insurance rep solemnly faces his partner. The older man'
- 'He grabs her hair and pulls her head back. She'
- 'A kid in blue shorts is vacuuming the floor. A kid in a red shirt'
|
2 |
- 'In slow motion, both the Russians and Americans celebrate. Someone'
- 'Through a window, we watch someone raise his teacup to his companions. At home, someone'
- 'As our view retracts through the star map a holographic line sets out from the gunner chair and targets hologram of the planet earth. She'
|
4 |
- "The waiter refills someone's glass. Someone"
- "He finds someone's records in a box. Someone"
- "Bloodstains spread over someone's white shirt. Someone"
|
7 |
- 'Now, someone stands below an overcast sky. Strands of his greasy black hair'
- 'Someone turns at the sound of the distant horns. 6000 horsemen, lead by people,'
- 'Someone points his wand upwards. High above, red sparks'
|
5 |
- 'Now in the eating quarters, someone faces a husky, larged - nosed cook. The cook'
- 'A logo for a sports even is shown. There'
- 'Someone stirs the cookie dough in a bowl. The dough'
|
0 |
- 'He shows a water bottle he has along with a brush, and uses the brush to remove snow from the dash window of a car and the water to remove any excess snow left on the windshield. Once finished, he'
- "Someone and someone step into a tent. Someone's mouth"
- 'Someone steps outside and opens an umbrella. Someone halts,'
|
8 |
- 'Someone peers out from the cabin. As she emerges, someone'
- 'He gently tries to pull up and then reel the fishing line out of the hole. He'
- 'A woman smiles at the camera. The woman'
|
1 |
- 'We see a title screen. We'
- 'A lot of people are sitting on terraces in a big field and people is walking in the entrance of a big stadium. men'
- 'We see the finished painting and a line of paints. We then'
|
Evaluation
Metrics
Label |
Accuracy |
all |
0.1656 |
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/Swag-multi-class-8")
preds = model("He approaches the object and reads a plaque on its side. Someone")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
6 |
14.0833 |
40 |
Label |
Training Sample Count |
0 |
8 |
1 |
8 |
2 |
8 |
3 |
8 |
4 |
8 |
5 |
8 |
6 |
8 |
7 |
8 |
8 |
8 |
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 |
0.0056 |
1 |
0.2013 |
- |
0.2778 |
50 |
0.1955 |
- |
0.5556 |
100 |
0.0693 |
- |
0.8333 |
150 |
0.0166 |
- |
1.1111 |
200 |
0.0369 |
- |
1.3889 |
250 |
0.0149 |
- |
1.6667 |
300 |
0.0095 |
- |
1.9444 |
350 |
0.0238 |
- |
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}
}