bhujith10/bert-large-uncased-setfit_finetuned

SetFit with google-bert/bert-large-uncased

This is a SetFit model trained on the bhujith10/multi_class_classification_dataset dataset that can be used for Text Classification. This SetFit model uses google-bert/bert-large-uncased as the Sentence Transformer embedding model. A SetFitHead 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: google-bert/bert-large-uncased
• —Classification head: a SetFitHead instance
• —Maximum Sequence Length: 512 tokens
• —Number of Classes: 6 classes
• —Training Dataset: bhujith10/multi_class_classification_dataset <!-- - 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

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("bhujith10/bert-large-uncased-setfit_finetuned")
# Run inference
preds = model("Title: On the isoperimetric quotient over scalar-flat conformal classes,
Abstract: Let $(M,g)$ be a smooth compact Riemannian manifold of dimension $n$ with
smooth boundary $\partial M$. Suppose that $(M,g)$ admits a scalar-flat
conformal metric. We prove that the supremum of the isoperimetric quotient over
the scalar-flat conformal class is strictly larger than the best constant of
the isoperimetric inequality in the Euclidean space, and consequently is
achieved, if either (i) $n\ge 12$ and $\partial M$ has a nonumbilic point; or
(ii) $n\ge 10$, $\partial M$ is umbilic and the Weyl tensor does not vanish at
some boundary point.")

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Training Details

Training Set Metrics

Training Hyperparameters

• —batch_size: (4, 4)
• —num_epochs: (2, 2)
• —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: True

Training Results

Framework Versions

• —Python: 3.10.12
• —SetFit: 1.1.0
• —Sentence Transformers: 3.3.1
• —Transformers: 4.45.2
• —PyTorch: 2.1.0+cu118
• —Datasets: 3.2.0
• —Tokenizers: 0.20.3

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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