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

Model Details

Model Description

• —Model Type: SetFit <!-- - Sentence Transformer: Unknown -->
• —Classification head: a LogisticRegression instance
• —Maximum Sequence Length: 512 tokens
• —Number of Classes: 2 classes <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

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

Model Labels

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("mahitha-t/text_classification_model")
# Run inference
preds = model("I guess I was attracted to this film both because of the sound of the story and the leading actor, so I gave it a chance, from director Gregor Jordan (Buffalo Soldiers). Basically Ned Kelly (Heath Ledger) is set up by the police, especially Superintendent Francis Hare (Geoffrey Rush), he is forced to go on the run forming a gang and go against them to clear his own and his family's names. That's really all I can say about the story, as I wasn't paying the fullest attention to be honest. Also starring Orlando Bloom as Joseph Byrne, Naomi Watts as Julia Cook, Laurence Kinlan as Dan Kelly, Philip Barantini as Steve Hart, Joel Edgerton as Aaron Sherritt, Kiri Paramore as Constable Fitzpatrick, Kerry Condon as Kate Kelly, Emily Browning as Grace Kelly and Rachel Griffiths as Susan Scott. Ledger makes a pretty good performance, for what it's worth, and the film does have it's eye-catching moments, particularly with a gun battle towards the end, but I can't say I enjoyed it as I didn't look at it all. Okay!")

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Training Details

Training Set Metrics

Training Hyperparameters

• —batch_size: (16, 2)
• —num_epochs: (1, 16)
• —max_steps: -1
• —sampling_strategy: oversampling
• —bodylearningrate: (2e-05, 1e-05)
• —headlearningrate: 0.01
• —loss: CosineSimilarityLoss
• —distancemetric: cosinedistance
• —margin: 0.25
• —endtoend: False
• —use_amp: False
• —warmup_proportion: 0.1
• —l2_weight: 0.01
• —seed: 42
• —evalmaxsteps: -1
• —loadbestmodelatend: False

Training Results

Framework Versions

• —Python: 3.11.13
• —SetFit: 1.1.2
• —Sentence Transformers: 4.1.0
• —Transformers: 4.52.4
• —PyTorch: 2.6.0+cu124
• —Datasets: 3.6.0
• —Tokenizers: 0.21.1

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}
}

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