Add model card for GovRelBERT

README.md

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+---
+pipeline_tag: text-ranking
+library_name: transformers
+license: other
+tags:
+  - bert
+  - modernbert
+  - government
+  - relevance
+  - text-relevance
+  - govrelbench
+---
+
+# GovRelBERT: A Model for Government Domain Relevance
+
+**GovRelBERT** is a specialized model designed for evaluating the core capabilities of Large Language Models (LLMs) in the government domain, particularly focusing on text relevance. It was introduced as part of the [GovRelBench: A Benchmark for Government Domain Relevance](https://huggingface.co/papers/2507.21419) paper.
+
+Built upon the **ModernBERT** architecture, GovRelBERT is trained using the **SoftGovScore** method. This innovative method converts hard labels into soft scores, enabling the model to accurately compute a text's government domain relevance score. GovRelBERT serves as a dedicated evaluation tool within the GovRelBench framework, aiming to improve the assessment of LLMs in government-related research and practical applications.
+
+The code and dataset are available as part of the GovRelBench project on GitHub: [https://github.com/pansysy/GovRelBench](https://github.com/pansysy/GovRelBench).
+
+## How to use
+
+You can use `GovRelBERT` with the Hugging Face `transformers` library to compute a government domain relevance score for a given text.
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "pansysy/GovRelBERT"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example 1: Text highly relevant to the government domain
+text_relevant = "The new policy initiative aims to streamline public services for citizens."
+inputs_relevant = tokenizer(text_relevant, return_tensors="pt")
+
+with torch.no_grad():
+    outputs_relevant = model(**inputs_relevant)
+    relevance_score_relevant = outputs_relevant.logits.item()
+
+print(f"Text: '{text_relevant}'")
+print(f"Government relevance score: {relevance_score_relevant:.4f}")
+
+print("-" * 50)
+
+# Example 2: Text less relevant to the government domain
+text_less_relevant = "How to train your pet dog to fetch a ball."
+inputs_less_relevant = tokenizer(text_less_relevant, return_tensors="pt")
+
+with torch.no_grad():
+    outputs_less_relevant = model(**inputs_less_relevant)
+    relevance_score_less_relevant = outputs_less_relevant.logits.item()
+
+print(f"Text: '{text_less_relevant}'")
+print(f"Government relevance score: {relevance_score_less_relevant:.4f}")
+```
+
+## Citation
+
+If you find this work helpful, please cite the original paper:
+
+```bibtex
+@misc{liu2025govrelbench,
+      title={GovRelBench:A Benchmark for Government Domain Relevance},
+      author={Yizhuo Liu and Siyi Pan and Ruohong Han and Yu Hong and Bojin Wang and Mingyang Li and Jianxun Tang},
+      year={2025},
+      eprint={2507.21419},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2507.21419},
+}
+```