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I'm trying HNM via hn_mine.py, but the hard negatives are gibberish. #1388

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seongjiko opened this issue Feb 28, 2025 · 0 comments

seongjiko commented Feb 28, 2025

Hi, I'm trying to do HNM via hn_mine.py. The dataset exists as below:

# sample.jsonl (120k rows)
{
    "query": "사채권자가 자본금 감소에 대하여 이의를 제기하려면 사채권자집회의 결의가 있어야 하나, 법원 ...(omitted)
    "pos": "아닙니다. 사채권자가 자본금 감소에 대하여 이의를 제기하려면 사채권자집회의 결의 ...(omitted)
}

python hn_mine.py \
--input_file sample.jsonl \
--output_file sample_output.jsonl \
--range_for_sampling 2-30 \
--negative_number 5 \
--use_gpu_for_searching \
--embedder_name_or_path .../models/bge-m3  \ (downloaded via hugging face git clone (BAAI/BGE-m3))
--embedder_model_class encoder-only-m3 \ (or none, tried both).

However, the following Hard Negative dataset was extracted:

{
    "query": "사채권자가 자본금 감소에 대하여 이의를 제기하려면 사채권자집회의 결의가 있어야 하나, 법원 ...(omitted)
    "pos": "아닙니다. 사채권자가 자본금 감소에 대하여 이의를 제기하려면 사채권자집회의 결의 ...(omitted)
    "neg": [
        "初",
        "샹",
        "듭",
        "試",
        "ち"
    ]
}

My dataset is fully natural language data. How can I solve this problem?

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