Upload README.md with huggingface_hub

README.md

@@ -1,223 +1,20 @@
 ---
+title: Mizan-Rerank-V2 Demo
+emoji: 🔍
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.29.0
+app_file: app.py
+pinned: false
 license: apache-2.0
-language:
-- ar
-- en
-base_model:
-- Alibaba-NLP/gte-multilingual-reranker-base
-tags:
-- sentence-transformers
-- cross-encoder
-- reranker
-- arabic
-- long-context
-pipeline_tag: text-ranking
-library_name: sentence-transformers
-inference: true
+models:
+- ALJIACHI/Mizan-Rerank-v2
+short_description: Arabic Long-Context Reranking Model Demo
 ---
 
-# Mizan-Rerank-v2
+# Mizan-Rerank-v2 Demo
 
-A high-performance open-source cross-encoder model for reranking Arabic long texts, fine-tuned from Alibaba-NLP/gte-multilingual-reranker-base with state-of-the-art results on Arabic reranking benchmarks.
+Interactive demo for **Mizan-Rerank-v2**, a 305M-parameter cross-encoder model for Arabic long-context text reranking.
 
-![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Mizan--Rerank--v2-blue)
-![Model Size](https://img.shields.io/badge/Parameters-305M-green)
-![License](https://img.shields.io/badge/License-Apache%202.0-brightgreen)
-
-## Overview
-
-Mizan-Rerank-v2 is a cross-encoder reranking model based on [Alibaba-NLP/gte-multilingual-reranker-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-reranker-base), specifically fine-tuned for Arabic text reranking. It excels at reranking long documents (up to 8192 tokens) and outperforms both its base model and larger competitors on Arabic reranking benchmarks.
-
-## Key Features
-
-- **Long Document Support**: Handles up to 8192 tokens using RoPE position embeddings with NTK scaling
-- **Superior Arabic Performance**: Outperforms BAAI/bge-reranker-v2-m3 (568M) despite being nearly half the size
-- **Arabic Language Optimization**: Fine-tuned on 1.2M+ Arabic query-document pairs from diverse sources
-
-## Performance Benchmarks
-
-![Reranker Benchmark Comparison](chart-1.png)
-
-### Reranking Evaluation (ndcg@10)
-
-| Model | Parameters | Reranking | Triplet | MIRACL (Long Docs) | WikiQA | MedQA |
-|-------|-----------|-----------|---------|---------------------|--------|-------|
-| **Mizan-Rerank-v2** | **305M** | **1.0000** | 0.9993 | **0.8091** | 0.8258 | **0.6775** |
-| BAAI/bge-reranker-v2-m3 | 568M | **1.0000** | **0.9998** | 0.7231 | **0.8669** | 0.6584 |
-| Alibaba-NLP/gte-multilingual-reranker-base | 305M | **1.0000** | 0.9991 | 0.7539 | 0.8275 | 0.6648 |
-| ALJIACHI/Mizan-Rerank-v1 | 149M | 0.9986 | 0.9955 | 0.7370 | 0.7739 | 0.5502 |
-
-### Key Improvements over Base Model
-
-| Benchmark | Base Model | Mizan-Rerank-v2 | Improvement |
-|-----------|-----------|-----------------|-------------|
-| Reranking | 1.0000 | 1.0000 | -- |
-| Triplet | 0.9991 | 0.9993 | +0.0002 |
-| MIRACL (Long Docs) | 0.7539 | 0.8091 | **+0.0552** |
-| WikiQA | 0.8275 | 0.8258 | -0.0017 |
-| MedQA | 0.6648 | 0.6775 | **+0.0127** |
-
-### Key Improvements over BAAI/bge-reranker-v2-m3
-
-| Benchmark | bge-reranker-v2-m3 | Mizan-Rerank-v2 | Improvement |
-|-----------|-------------------|-----------------|-------------|
-| Reranking | 1.0000 | 1.0000 | -- |
-| Triplet | 0.9998 | 0.9993 | -0.0005 |
-| MIRACL (Long Docs) | 0.7231 | 0.8091 | **+0.0860** |
-| WikiQA | 0.8669 | 0.8258 | -0.0411 |
-| MedQA | 0.6584 | 0.6775 | **+0.0191** |
-
-## Model Details
-
-- **Model Type:** Cross Encoder
-- **Base Model:** [Alibaba-NLP/gte-multilingual-reranker-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-reranker-base)
-- **Architecture:** NewForSequenceClassification (12 layers, 768 hidden, 12 heads)
-- **Maximum Sequence Length:** 8192 tokens
-- **Position Embeddings:** RoPE with NTK scaling (factor 8.0)
-- **Number of Output Labels:** 1
-- **Language:** Arabic (ar), English (en)
-- **License:** Apache 2.0
-
-## Usage
-
-### Using Sentence Transformers
-
-```bash
-pip install -U sentence-transformers
-```
-
-```python
-from sentence_transformers import CrossEncoder
-
-# Load model
-model = CrossEncoder("ALJIACHI/Mizan-Rerank-v2", max_length=8192, trust_remote_code=True)
-
-# Score query-document pairs
-pairs = [
-    ["ما هو تفسير الآية وجعلنا من الماء كل شيء حي",
-     "تعني الآية أن الماء هو عنصر أساسي في حياة جميع الكائنات الحية، وهو ضروري لاستمرار الحياة."],
-    ["ما هو تفسير الآية وجعلنا من الماء كل شيء حي",
-     "تم اكتشاف كواكب خارج المجموعة الشمسية تحتوي على مياه متجمدة."],
-    ["ما هو تفسير الآية وجعلنا من الماء كل شيء حي",
-     "تحدث القرآن الكريم عن البرق والرعد في عدة مواضع مختلفة."],
-]
-
-scores = model.predict(pairs)
-print(scores)
-# High score for the relevant passage, low scores for irrelevant ones
-
-# Or rank documents for a query
-ranks = model.rank(
-    "ما هو تفسير الآية وجعلنا من الماء كل شيء حي",
-    [
-        "تعني الآية أن الماء هو عنصر أس��سي في حياة جميع الكائنات الحية، وهو ضروري لاستمرار الحياة.",
-        "تم اكتشاف كواكب خارج المجموعة الشمسية تحتوي على مياه متجمدة.",
-        "تحدث القرآن الكريم عن البرق والرعد في عدة مواضع مختلفة.",
-    ]
-)
-print(ranks)
-# [{'corpus_id': 0, 'score': ...}, {'corpus_id': 1, 'score': ...}, ...]
-```
-
-### Using Transformers Directly
-
-```python
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import torch
-
-model = AutoModelForSequenceClassification.from_pretrained(
-    "ALJIACHI/Mizan-Rerank-v2",
-    trust_remote_code=True,
-    torch_dtype=torch.float16,
-)
-tokenizer = AutoTokenizer.from_pretrained("ALJIACHI/Mizan-Rerank-v2")
-
-def get_relevance_score(query, passage):
-    inputs = tokenizer(query, passage, return_tensors="pt", padding=True, truncation=True, max_length=8192)
-    with torch.no_grad():
-        outputs = model(**inputs)
-    return torch.sigmoid(outputs.logits).item()
-
-query = "ما هي فوائد فيتامين د؟"
-passages = [
-    "يساعد فيتامين د في تعزيز صحة العظام وتقوية الجهاز المناعي، كما يلعب دوراً مهماً في امتصاص الكالسيوم.",
-    "يستخدم فيتامين د في بعض الصناعات الغذائية كمادة حافظة.",
-    "أطلقت وزارة الزراعة حملة وطنية لزيادة الوعي بأهمية الزراعة العضوية.",
-]
-
-scores = [(p, get_relevance_score(query, p)) for p in passages]
-reranked = sorted(scores, key=lambda x: x[1], reverse=True)
-
-for passage, score in reranked:
-    print(f"Score: {score:.4f} | {passage[:80]}...")
-```
-
-## Training Details
-
-### Training Data
-
-Trained on **1,199,634 query-document pairs** from diverse Arabic sources
-
-### Training Configuration
-
-| Parameter | Value |
-|-----------|-------|
-| Base Model | Alibaba-NLP/gte-multilingual-reranker-base |
-| Max Sequence Length | 8192 |
-| Batch Size | 2 |
-| Gradient Accumulation Steps | 16 |
-| Effective Batch Size | 32 |
-| Learning Rate | 5e-7 |
-| LR Scheduler | Cosine |
-| Warmup Ratio | 0.1 |
-| Precision | FP16 |
-| Gradient Checkpointing | Enabled |
-| Loss Function | BinaryCrossEntropyLoss (pos_weight=1.24) |
-
-
-## Applications
-
-- Arabic search engines and information retrieval systems
-- RAG (Retrieval-Augmented Generation) pipelines
-- Islamic text search and jurisprudence Q&A
-- Digital library and archive search
-- Long-document Arabic content analysis
-- E-learning platforms with Arabic content
-
-### Framework Versions
-
-- Python: 3.10.14
-- Sentence Transformers: 5.4.1
-- Transformers: 4.55.4
-- PyTorch: 2.8.0+cu126
-- Accelerate: 1.10.0
-- Datasets: 3.5.0
-- Tokenizers: 0.21.0
-
-## Citation
-
-```bibtex
-@software{Mizan_Rerank_v2_2026,
-  author = {Ali Aljiachi},
-  title = {Mizan-Rerank-v2: Arabic Long-Context Text Reranking Model},
-  year = {2026},
-  publisher = {Hugging Face},
-  url = {https://huggingface.co/ALJIACHI/Mizan-Rerank-v2}
-}
-```
-
-```bibtex
-@inproceedings{reimers-2019-sentence-bert,
-    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
-    author = "Reimers, Nils and Gurevych, Iryna",
-    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
-    month = "11",
-    year = "2019",
-    publisher = "Association for Computational Linguistics",
-    url = "https://arxiv.org/abs/1908.10084",
-}
-```
-
-## License
-
-Released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
+Enter a query and a list of documents (one per line) to see them reranked by relevance.