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--- |
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license: cc-by-4.0 |
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language: |
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- en |
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pipeline_tag: text-classification |
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tags: |
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- RoBERTa-large |
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- topic |
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- news |
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--- |
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# Fine-tuned RoBERTa-large for detecting news on government regulation |
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# Model Description |
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This model is a finetuned RoBERTa-large, for classifying whether news articles are about government regulation. |
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# How to Use |
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```python |
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from transformers import pipeline |
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classifier = pipeline("text-classification", model="dell-research-harvard/topic-govt_regulation") |
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classifier("Senate passes gun control bill") |
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``` |
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# Training data |
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The model was trained on a hand-labelled sample of data from the [NEWSWIRE dataset](https://huggingface.co/datasets/dell-research-harvard/newswire). |
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Split|Size |
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-|- |
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Train|612 |
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Dev|131 |
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Test|131 |
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# Test set results |
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Metric|Result |
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-|- |
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F1|0.8750 |
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Accuracy|0.9237 |
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Precision|0.7955 |
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Recall|0.9722 |
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# Citation Information |
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You can cite this dataset using |
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``` |
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@misc{silcock2024newswirelargescalestructureddatabase, |
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title={Newswire: A Large-Scale Structured Database of a Century of Historical News}, |
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author={Emily Silcock and Abhishek Arora and Luca D'Amico-Wong and Melissa Dell}, |
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year={2024}, |
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eprint={2406.09490}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2406.09490}, |
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} |
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``` |
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# Applications |
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We applied this model to a century of historical news articles. You can see all the classifications in the [NEWSWIRE dataset](https://huggingface.co/datasets/dell-research-harvard/newswire). |
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