carlesoctav/SentimentClassifierDune-8shot

SetFit with sentence-transformers/paraphrase-mpnet-base-v2 on data/raw/15239678.jsonl

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. 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 body: sentence-transformers/paraphrase-mpnet-base-v2
• —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/SentimentClassifierDune-8shot")
# Run inference
preds = model("great movie with holes in the plot dune 2 is a great movie that can be enjoyed despite some holes in the plot and questionable story elements. the movie is great visually and serves as an example of how cgi doesn't have to make a movie seem sterile and fake, or like watching a video game like the new brand of marvel movies and aquaman do.the cast is great - christopher walken should have had more scenes and other than it being him, his performance was not unique in any way - and there is a non credited appearance that i'm not going to spoil that is a wow!the movie is long, but there's a difference between long and too long, and any movie that wants to be an epic. 2 out of 5 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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