gmenchetti/bart-large-mnli-empathy

SetFit with facebook/bart-large-mnli

This is a SetFit model that can be used for Text Classification. This SetFit model uses facebook/bart-large-mnli 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: facebook/bart-large-mnli
• —Classification head: a LogisticRegression instance
• —Maximum Sequence Length: 1024 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("gmenchetti/bart-large-mnli-empathy")
# Run inference
preds = model("Hello Jonathan, I hope you day is going well. The purpose of this msg is to improve your communication regarding your work on the Beta Project. You are important which is why we need to make sure that your thoughts and Ideas are clearly communicated with helpful factual info. I want to get your thoughts on how you best communicate and your thoughts on how to communicate more concisely. Please come up with 2-3 suggestions as will I and lets set up a time within the next 48 hours that you and I can build a plan that will help ensure your great work is being understood for the success of Beta. I am confident that we will develop a plan that continues allow your work to help the program. Please meg me what time works best for you when you end your travel. Best, William")

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Training Details

Training Set Metrics

Training Hyperparameters

• —batch_size: (4, 4)
• —num_epochs: (3, 3)
• —max_steps: -1
• —sampling_strategy: oversampling
• —num_iterations: 20
• —bodylearningrate: (2e-05, 2e-05)
• —headlearningrate: 2e-05
• —loss: CosineSimilarityLoss
• —distancemetric: cosinedistance
• —margin: 0.25
• —endtoend: False
• —use_amp: False
• —warmup_proportion: 0.1
• —seed: 42
• —evalmaxsteps: -1
• —loadbestmodelatend: False

Training Results

Framework Versions

• —Python: 3.10.13
• —SetFit: 1.0.3
• —Sentence Transformers: 2.6.1
• —Transformers: 4.39.3
• —PyTorch: 2.0.0.post200
• —Datasets: 2.16.1
• —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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