vidhi0206/setfit-paraphrase-mpnet-amazon_cf

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:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

• Model Type: SetFit
• Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 2 classes

Model Sources

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
0	'Well, I wore these under my dress and I must say they fit well and I received several compliments .' 'Gildan makes a sweatshirt as they should be made.' 'It is very pretty except for the dark color of the felt that was provided for the reindeer.'
1	'If it had a weighted bottom I would have given it 4/5 stars.' "I can definitely wear a t-shirt over this bra without the bra showing, but I wish it were padded so nipples don't show through shirts that are more fitted." '"But oddly enough, the bottoms are a little too loose in the waist (37\) and could have used another inch or two in the inseam ( I normally take a 35\"" or 36\"" in jeans, depending on the brand if this helps)."""'

Evaluation

Metrics

Label	Accuracy
all	0.7284

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

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("vidhi0206/setfit-paraphrase-mpnet-amazon_cf")
# Run inference
preds = model("I wished it had the output on back instead of on the side.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	9	21.875	50

Label	Training Sample Count
0	8
1	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
0.0125	1	0.2688	-
0.625	50	0.0015	-

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}
}