动手学习RAG：大模型重排模型 bge-reranker-v2-

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1. 环境准备

pip install transformers
pip install open-retrievals

• 注意安装时是pip install open-retrievals，但调用时只需要import retrievals
• 欢迎关注最新的更新 https://github.com/LongxingTan/open-retrievals

2. 使用大模型做重排

from retrievals import LLMRanker

model_name = 'BAAI/bge-reranker-v2-gemma'
model = LLMRanker.from_pretrained(
            model_name,
            causal_lm=True,
            use_fp16=True,
        )

score = model.compute_score(['query', 'passage'])
print(score)

scores = model.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)

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3. 微调

MODEL_NAME='BAAI/bge-reranker-v2-gemma'
TRAIN_DATA="/root/kag101/src/open-retrievals/t2/t2_ranking.jsonl"
OUTPUT_DIR="/root/kag101/src/open-retrievals/t2/ft_out"

torchrun --nproc_per_node 1 \
    -m retrievals.pipelines.rerank \
    --output_dir ${OUTPUT_DIR} \
    --overwrite_output_dir \
    --model_name_or_path $MODEL_NAME \
    --model_type llm \
    --causal_lm True \
    --use_lora True \
    --data_name_or_path $TRAIN_DATA \
    --task_prompt "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'." \
    --query_instruction "A: " \
    --document_instruction 'B: ' \
    --positive_key positive \
    --negative_key negative \
    --learning_rate 2e-4 \
    --num_train_epochs 3 \
    --per_device_train_batch_size 4 \
    --gradient_accumulation_steps 16 \
    --dataloader_drop_last True \
    --max_len 256 \
    --train_group_size 4 \
    --logging_steps 10 \
    --save_steps 20000 \
    --save_total_limit 1 \
    --bf16

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4. 评测

在C-MTEB中进行评测。微调前保留10%的数据集作为测试集验证

微调前的指标：
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微调后的指标：

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{
  "dataset_revision": null,
  "mteb_dataset_name": "CustomReranking",
  "mteb_version": "1.1.1",
  "test": {
    "evaluation_time": 77.35,
    "map": 0.7057362287508586,
    "mrr": 0.8166538440773136
  }
}

微调后map从0.637上升至0.706，mrr从0.734上升至0.816