news_fp_disambiguation_model

library_name: setfit Base model: model_dataset_training_golden_records_jj_23022024_v4 dataset test: test_filter_golden_records_jj_23022024_test_dataset_v4

SetFit with TaylorAI/bge-micro-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses TaylorAI/bge-micro-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. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

library_name: setfit Base model: model_dataset_training_golden_records_jj_23022024_v4 dataset test: test_filter_golden_records_jj_23022024_test_dataset_v4

Model Description

• Model Type: SetFit
• Sentence Transformer body: TaylorAI/bge-micro-v2
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 tokens

<!-- - **Number of Classes:** Unknown --> <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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

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("davidAtdm/model_dataset_training_golden_records_jj_23022024_v4")
# Run inference
preds = model("I loved the spiderman movie!")

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Training Details

Framework Versions

• Python: 3.9.16
• SetFit: 1.0.3
• Sentence Transformers: 2.4.0
• Transformers: 4.38.1
• PyTorch: 2.2.1+cu121
• Datasets: 2.17.1
• 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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