anismahmahi/Roberta-large-G3-setfit-model

SetFit

This is a SetFit model that can be used for Text Classification. 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
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 256 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
0.0	'Pamela Geller and Robert Spencer co-founded anti-Muslim group Stop Islamization of America.\n' 'He added: "We condemn all those whose behaviours and views run counter to our shared values and will not stand for extremism in any form."\n' 'Ms Geller, of the Atlas Shrugs blog, and Mr Spencer, of Jihad Watch, are also co-founders of the American Freedom Defense Initiative, best known for a pro-Israel "Defeat Jihad" poster campaign on the New York subway.\n'
1.0	'On both of their blogs the pair called their bans from entering the UK "a striking blow against freedom" and said the "the nation that gave the world the Magna Carta is dead".\n' 'A researcher with the organisation, Matthew Collins, said it was "delighted" with the decision.\n' 'Lead attorney Matt Gonzalez has argued that the weapon was a SIG Sauer with a "hair trigger in single-action mode" — a model well-known for accidental discharges even among experienced shooters.\n'

Evaluation

Metrics

Label	F1
all	0.3372

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("anismahmahi/Roberta-large-G3-setfit-model")
# Run inference
preds = model("There are 2 trillion Google searches per day.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	26.8625	105

Label	Training Sample Count
0	200
1	200

Training Hyperparameters

• batch_size: (8, 8)
• num_epochs: (3, 3)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 5
• 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
0.002	1	0.3467	-
0.1	50	0.2333	-
0.2	100	0.237	-
0.3	150	0.2466	-
0.4	200	0.208	-
0.5	250	0.2121	-
0.6	300	0.0076	-
0.7	350	0.0011	-
0.8	400	0.0007	-
0.9	450	0.0002	-
1.0	500	0.0015	0.3342
1.1	550	0.0001	-
1.2	600	0.0002	-
1.3	650	0.0003	-
1.4	700	0.0003	-
1.5	750	0.0002	-
1.6	800	0.0002	-
1.7	850	0.0001	-
1.8	900	0.0001	-
1.9	950	0.0001	-
2.0	1000	0.0001	0.3303
2.1	1050	0.0	-
2.2	1100	0.0	-
2.3	1150	0.0001	-
2.4	1200	0.0	-
2.5	1250	0.0	-
2.6	1300	0.0	-
2.7	1350	0.0001	-
2.8	1400	0.0001	-
2.9	1450	0.0	-
3.0	1500	0.0	0.3327

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• SetFit: 1.0.2
• Sentence Transformers: 2.2.2
• Transformers: 4.35.2
• PyTorch: 2.1.0+cu121
• Datasets: 2.16.1
• 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}
}