carlesoctav/SentimentClassifierBarbieDune-8shot

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: en
• —License: apache-2.0

Model Sources

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

Model Labels

Evaluation

Metrics

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("carlesoctav/SentimentClassifierBarbieDune-8shot")
# Run inference
preds = model("decent i like what they did with this movie and the characters with its combining the barbie world and the real world. barbie starts getting \"vibes\" and has to go into the real world to find the girl who played with her to set things right and winds up in the mattel headquarters. something resembling chaos ensues. ken joins her and winds up causing further damage. i like what they do in various stages of the story and with the characters. it was overall a very pleasant surprise snd a good movie with a good cast. margot robbie, ryan gosling, america ferrera, and will ferrell were all good in their roles. if you are a movie and/or a barbie fan, you will love this movie.*** out of **** 2 out of 7 found this helpful. was this review helpful? sign in to vote. permalink")

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Training Details

Training Set Metrics

Training Hyperparameters

• —batch_size: (16, 16)
• —num_epochs: (1, 1)
• —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
• —seed: 42
• —evalmaxsteps: -1
• —loadbestmodelatend: True

Training Results

• —The bold row denotes the saved checkpoint.

Framework Versions

• —Python: 3.10.11
• —SetFit: 1.0.3
• —Sentence Transformers: 2.5.1
• —Transformers: 4.38.2
• —PyTorch: 2.0.1
• —Datasets: 2.18.0
• —Tokenizers: 0.15.2

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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