Duplicate from cea-list-ia/glidre_multi

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README.md

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+---
+license: apache-2.0
+datasets:
+- HuggingFaceFW/fineweb
+- HuggingFaceFW/fineweb-2
+language:
+- en
+- sq
+- ar
+- cs
+- fr
+- de
+- it
+- ro
+- es
+- sl
+- tr
+- sr
+base_model:
+- microsoft/mdeberta-v3-base
+- Alibaba-NLP/gte-multilingual-base
+tags:
+- relation-extraction
+---
+# GLiDRE: Generalist and Lightweight Model for Document Relation Extraction
+## Overview
+GLiDRE is a generalist and lightweight model designed for Document Relation Extraction. It enables users to extract and classify relationships among entities within unstructured text documents. Built upon the success of previous work [GLiNER](https://github.com/urchade/GLiNER).
+
+## Key Features
+- **Zero-Shot Extraction:** Capable of classifying unseen relations directly from text.
+- **Versatile Input Handling:** Compatible with both tokenized text and full documents.
+- **Customizable Architecture:** Supports multiple loss functions and allows easy modification of model components.
+
+## Installation
+
+Install [GLiDRE](https://github.com/cea-list-lasti/glidre)
+```bash
+pip install .
+```
+
+## Quick Start
+
+Here's a simple Python example to get you started:
+```python
+from glidre import GLiDRE
+
+model = GLiDRE.from_pretrained("cea-list-ia/glidre_large")
+
+text = "The Loud Tour was the fourth overall and third world concert tour by Barbadian recording artist Rihanna."
+
+# Define relation labels
+labels = ["COUNTRY_OF_CITIZENSHIP", "PUBLICATION_DATE", "PART_OF"] # Labels are uppercase because the model performs better with capitalized relation names
+# Define entity mentions (format: [{"id" : id, "type" : type, "mentions" : [{"value" : text, "start" : start_idx, "end" : end_idx}]}])
+mentions = [{
+                "id": 0,
+                "mentions": [
+                    {
+                        "value": "Barbadian",
+                        "start": 69,
+                        "end": 78
+                    }
+                ],
+                "type": "LOC"
+            },
+            {
+                "id": 1,
+                "mentions": [
+                    {
+                        "value": "Rihanna",
+                        "start": 96,
+                        "end": 103
+                    }
+                ],
+                "type": "PER"}]
+
+# Predict relations using GLiDRE
+relations = model.predict_entities(text = text, labels = labels, mentions = mentions, threshold=0.3, multi_label = False)
+print("Predicted Relations:")
+for relation in relations:
+    print(relation["entity_1"])
+    print("Label :",  relation["relation_type"])
+    print(relation["entity_2"])
+    print("---")
+```
+
+## Training
+
+GLiDRE supports training on various datasets such as DocRED and Re-DocRED.
+```bash
+# For Re-DocRED:
+python3 train.py --config configs/config_finetuning.yaml
+```
+
+## Citation
+
+```bibtex
+@misc{armingaud2025glidregeneralistlightweightmodel,
+      title={GLiDRE: Generalist Lightweight model for Document-level Relation Extraction}, 
+      author={Robin Armingaud and Romaric Besançon},
+      year={2025},
+      eprint={2508.00757},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2508.00757}, 
+}
+```

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