Add library name and paper link, improve description

This PR adds the `library_name` to the metadata, adds a direct link to the paper, improves the description in the model card and adds project page URL. The model uses the Transformers library, and the addition of the `library_name` will help users easily identify this.

README.md

@@ -1,13 +1,23 @@
 ---
-license: mit
+base_model:
+- google-bert/bert-base-chinese
 datasets:
 - china-ai-law-challenge/cail2018
 language:
 - zh
+license: mit
 metrics:
 - accuracy
 - f1
-base_model:
-- google-bert/bert-base-chinese
 pipeline_tag: text-classification
----
+library_name: transformers
+---
+
+# ⚖️ LegalDuet: Learning Effective Representations for Legal Judgment Prediction
+
+This model, presented in the paper [LegalDuet: Learning Effective Representations for Legal Judgment Prediction through a Dual-View Legal Clue Reasoning](https://huggingface.co/papers/2401.15371), enhances the accuracy of Legal Judgment Prediction (LJP) by employing a dual-view legal reasoning mechanism. This mechanism combines Law Case Reasoning (using past legal decisions) and Legal Ground Reasoning (matching legal clues between criminal cases and legal decisions) to create a tailored embedding space for improved legal judgment prediction. LegalDuet achieves state-of-the-art performance on the CAIL2018 dataset.
+
+The model is based on `google-bert/bert-base-chinese` and can be loaded using the Transformers library.
+
+Code: https://github.com/NEUIR/LegalDuet
+Project Page: http://cail.cipsc.org.cn/task_summit.html?raceID=1&cail_tag=2018