Create README.md

README.md

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