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README.md
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---
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license: mit
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datasets:
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- hotchpotch/JQaRA
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library_name: sentence-transformers
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---
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---
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license: mit
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datasets:
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- hotchpotch/JQaRA
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library_name: sentence-transformers
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---
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## hotchpotch/japanese-reranker-cross-encoder-small-v1
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日本語で学習させた Reranker (CrossEncoder) シリーズです。
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| モデル名 | layers | hidden_size |
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| ----------------------------------------------------------------------------------------------------------------------------------- | ------ | ----------- |
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| [hotchpotch/japanese-reranker-cross-encoder-xsmall-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-xsmall-v1) | 6 | 384 |
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| [hotchpotch/japanese-reranker-cross-encoder-small-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-small-v1) | 12 | 384 |
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| [hotchpotch/japanese-reranker-cross-encoder-base-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-base-v1) | 12 | 768 |
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| [hotchpotch/japanese-reranker-cross-encoder-large-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-large-v1) | 24 | 1024 |
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| [hotchpotch/japanese-bge-reranker-v2-m3-v1](https://huggingface.co/hotchpotch/japanese-bge-reranker-v2-m3-v1) | 24 | 1024 |
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Reranker についてや、技術レポート・評価等は以下を参考ください。
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- [日本語最高性能のRerankerをリリース / そもそも Reranker とは?](https://secon.dev/entry/2024/04/02/070000-japanese-reranker-release/)
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- [日本語 Reranker 作成のテクニカルレポート](https://secon.dev/entry/2024/04/02/080000-japanese-reranker-tech-report/)
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## 使い方
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### SentenceTransformers
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```python
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from sentence_transformers import CrossEncoder
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import torch
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MODEL_NAME = "hotchpotch/japanese-reranker-cross-encoder-small-v1"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = CrossEncoder(MODEL_NAME, max_length=512, device=device)
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if device == "cuda":
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model.model.half()
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query = "感動的な映画について"
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passages = [
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"深いテーマを持ちながらも、観る人の心を揺さぶる名作。登場人物の心情描写が秀逸で、ラストは涙なしでは見られない。",
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"重要なメッセージ性は評価できるが、暗い話が続くので気分が落ち込んでしまった。もう少し明るい要素があればよかった。",
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"どうにもリアリティに欠ける展開が気になった。もっと深みのある人間ドラマが見たかった。",
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"アクションシーンが楽しすぎる。見ていて飽きない。ストーリーはシンプルだが、それが逆に良い。",
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]
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scores = model.predict([(query, passage) for passage in passages])
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```
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## HuggingFace transformers
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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from torch.nn import Sigmoid
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MODEL_NAME = "hotchpotch/japanese-reranker-cross-encoder-small-v1"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
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model.to(device)
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model.eval()
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if device == "cuda":
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model.half()
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query = "感動的な映画について"
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passages = [
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"深いテーマを持ちながらも、観る人の心を揺さぶる名作。登場人物の心情描写が秀逸で、ラストは涙なしでは見られない。",
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"重要なメッセージ性は評価できるが、暗い話が続くので気分が落ち込んでしまった。もう少し明るい要素があればよかった。",
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"どうにもリアリティに欠ける展開が気になった。もっと深みのある人間ドラマが見たかった。",
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"アクションシーンが楽しすぎる。見ていて飽きない。ストーリーはシンプルだが、それが逆に良い。",
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]
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inputs = tokenizer(
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[(query, passage) for passage in passages],
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padding=True,
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truncation=True,
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max_length=512,
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return_tensors="pt",
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)
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inputs = {k: v.to(device) for k, v in inputs.items()}
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logits = model(**inputs).logits
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activation = Sigmoid()
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scores = activation(logits).squeeze().tolist()
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```
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## 評価結果
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| Model Name | [JQaRA](https://huggingface.co/datasets/hotchpotch/JQaRA) | [JaCWIR](https://huggingface.co/datasets/hotchpotch/JaCWIR) | [MIRACL](https://huggingface.co/datasets/miracl/miracl) | [JSQuAD](https://github.com/yahoojapan/JGLUE) |
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| ------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------- | ----------------------------------------------------------- | ------------------------------------------------------- | --------------------------------------------- |
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| [japanese-reranker-cross-encoder-xsmall-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-xsmall-v1) | 0.6136 | 0.9376 | 0.7411 | 0.9602 |
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| [japanese-reranker-cross-encoder-small-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-small-v1) | 0.6247 | 0.939 | 0.7776 | 0.9604 |
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| [japanese-reranker-cross-encoder-base-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-base-v1) | 0.6711 | 0.9337 | 0.818 | 0.9708 |
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| [japanese-reranker-cross-encoder-large-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-large-v1) | 0.7099 | 0.9364 | 0.8406 | 0.9773 |
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| [japanese-bge-reranker-v2-m3-v1](https://huggingface.co/hotchpotch/japanese-bge-reranker-v2-m3-v1) | 0.6918 | 0.9372 | 0.8423 | 0.9624 |
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| [bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | 0.673 | 0.9343 | 0.8374 | 0.9599 |
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| [bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 0.4718 | 0.7332 | 0.7666 | 0.7081 |
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| [bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 0.2445 | 0.4905 | 0.6792 | 0.5757 |
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| [cross-encoder-mmarco-mMiniLMv2-L12-H384-v1](https://huggingface.co/corrius/cross-encoder-mmarco-mMiniLMv2-L12-H384-v1) | 0.5588 | 0.9211 | 0.7158 | 0.932 |
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| [shioriha-large-reranker](https://huggingface.co/cl-nagoya/shioriha-large-reranker) | 0.5775 | 0.8458 | 0.8084 | 0.9262 |
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| [bge-m3+all](https://huggingface.co/BAAI/bge-m3) | 0.576 | 0.904 | 0.7926 | 0.9226 |
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| [bge-m3+dense](https://huggingface.co/BAAI/bge-m3) | 0.539 | 0.8642 | 0.7753 | 0.8815 |
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| [bge-m3+colbert](https://huggingface.co/BAAI/bge-m3) | 0.5656 | 0.9064 | 0.7902 | 0.9297 |
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| [bge-m3+sparse](https://huggingface.co/BAAI/bge-m3) | 0.5088 | 0.8944 | 0.6941 | 0.9184 |
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| [JaColBERTv2](https://huggingface.co/bclavie/JaColBERTv2) | 0.5847 | 0.9185 | 0.6861 | 0.9247 |
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| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 0.554 | 0.8759 | 0.7722 | 0.8892 |
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| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 0.4917 | 0.869 | 0.7025 | 0.8565 |
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| bm25 | 0.458 | 0.8408 | 0.4387 | 0.9002 |
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## ライセンス
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MIT License
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