sam123116/trained_model

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: 10 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
5	'Why did my payment fail?' 'Why is my payment not processing?' 'What are common payment issues?'
0	'Your products are not good' 'Your service is bad'
7	'Good Bye' 'Take care' 'Bye'
8	'Thanks'
9	'23234dfdff' 'abc' 'djfnknf'
4	'My order ref is ' 'How can I track my order?' 'Order Ref is C-123-P'
6	'What is your refund policy?' 'How can I get a refund?'
3	'order email' 'resend order email' 'I didnt receive my order email'
2	'How are you'
1	'Hi' 'Wassup' 'Hello'

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("setfit_model_id")
# Run inference
preds = model("Bye")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	3.4737	6

Label	Training Sample Count
0	2
1	3
2	1
3	3
4	12
5	3
6	2
7	3
8	1
9	8

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (5, 5)
• 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
• l2_weight: 0.01
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0105	1	0.2387	-
0.5263	50	0.1358	-
1.0526	100	0.0206	-
1.5789	150	0.0048	-
2.1053	200	0.0037	-
2.6316	250	0.0023	-
3.1579	300	0.002	-
3.6842	350	0.0017	-
4.2105	400	0.0024	-
4.7368	450	0.0015	-

Framework Versions

• Python: 3.10.13
• SetFit: 1.1.1
• Sentence Transformers: 3.4.0
• Transformers: 4.48.1
• PyTorch: 2.5.1
• Datasets: 3.2.0
• Tokenizers: 0.21.0

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}
}