Edit model card

HeackMT5-ZhSum100k: A Summarization Model for Chinese Texts

This model, heack/HeackMT5-ZhSum100k, is a fine-tuned mT5 model for Chinese text summarization tasks. It was trained on a diverse set of Chinese datasets and is able to generate coherent and concise summaries for a wide range of texts.

Model Details

  • Model: mT5
  • Language: Chinese
  • Training data: Mainly Chinese Financial News Sources, NO BBC or CNN source. Training data contains 1M lines.
  • Finetuning epochs: 10

Evaluation Results

The model achieved the following results:

  • ROUGE-1: 56.46
  • ROUGE-2: 45.81
  • ROUGE-L: 52.98
  • ROUGE-Lsum: 20.22

Usage

Here is how you can use this model for text summarization:

from transformers import MT5ForConditionalGeneration, T5Tokenizer

model = MT5ForConditionalGeneration.from_pretrained("heack/HeackMT5-ZhSum100k")
tokenizer = T5Tokenizer.from_pretrained("heack/HeackMT5-ZhSum100k")

chunk = """
财联社5月22日讯,据平安包头微信公众号消息,近日,包头警方发布一起利用人工智能(AI)实施电信诈骗的典型案例,福州市某科技公司法人代表郭先生10分钟内被骗430万元。
4月20日中午,郭先生的好友突然通过微信视频联系他,自己的朋友在外地竞标,需要430万保证金,且需要公对公账户过账,想要借郭先生公司的账户走账。
基于对好友的信任,加上已经视频聊天核实了身份,郭先生没有核实钱款是否到账,就分两笔把430万转到了好友朋友的银行卡上。郭先生拨打好友电话,才知道被骗。骗子通过智能AI换脸和拟声技术,佯装好友对他实施了诈骗。
值得注意的是,骗子并没有使用一个仿真的好友微信添加郭先生为好友,而是直接用好友微信发起视频聊天,这也是郭先生被骗的原因之一。骗子极有可能通过技术手段盗用了郭先生好友的微信。幸运的是,接到报警后,福州、包头两地警银迅速启动止付机制,成功止付拦截336.84万元,但仍有93.16万元被转移,目前正在全力追缴中。
"""
inputs = tokenizer.encode("summarize: " + chunk, return_tensors='pt', max_length=512, truncation=True)
summary_ids = model.generate(inputs, max_length=150, num_beams=4, length_penalty=1.5, no_repeat_ngram_size=2)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

print(summary)

包头警方发布一起利用AI实施电信诈骗典型案例:法人代表10分钟内被骗430万元

If you need a longer abbreviation, refer to the following code 如果需要更长的缩略语,参考如下代码:

from transformers import MT5ForConditionalGeneration, T5Tokenizer

model_heack = MT5ForConditionalGeneration.from_pretrained("heack/HeackMT5-ZhSum100k")
tokenizer_heack = T5Tokenizer.from_pretrained("heack/HeackMT5-ZhSum100k")


def _split_text(text, length):
    chunks = []
    start = 0
    while start < len(text):
        if len(text) - start > length:
            pos_forward = start + length
            pos_backward = start + length
            pos = start + length
            while (pos_forward < len(text)) and (pos_backward >= 0) and (pos_forward < 20 + pos) and  (pos_backward + 20 > pos) and text[pos_forward] not in {'.', '。',',',','} and text[pos_backward] not in {'.', '。',',',','}:
                pos_forward += 1
                pos_backward -= 1
            if pos_forward - pos >= 20 and pos_backward <= pos - 20:
                pos = start + length
            elif text[pos_backward] in {'.', '。',',',','}:
                pos = pos_backward
            else:
                pos = pos_forward
            chunks.append(text[start:pos+1])
            start = pos + 1
        else:
            chunks.append(text[start:])
            break
    # Combine last chunk with previous one if it's too short
    if len(chunks) > 1 and len(chunks[-1]) < 100:
        chunks[-2] += chunks[-1]
        chunks.pop()
    return chunks

def get_summary_heack(text, each_summary_length=150):
    chunks = _split_text(text, 300)
    summaries = []
    for chunk in chunks:
        inputs = tokenizer_heack.encode("summarize: " + chunk, return_tensors='pt', max_length=512, truncation=True)
        summary_ids = model_heack.generate(inputs, max_length=each_summary_length, num_beams=4, length_penalty=1.5, no_repeat_ngram_size=2)
        summary = tokenizer_heack.decode(summary_ids[0], skip_special_tokens=True)
        summaries.append(summary)
    return " ".join(summaries)

Credits

This model is trained and maintained by KongYang from Shanghai Jiao Tong University. For any questions, please reach out to me at my WeChat ID: kongyang.

License

This model is released under the CC BY-NC-SA 4.0 license. 并且: 若用于商业目的,使用本作品前必须获得以下微信账号的授权。未经授权使用将按照每千个字符0.1元的标准收费。 And: For commercial purposes, authorization must be obtained from the WeChat account below before using this work. Unauthorized use will be charged at a rate of 0.1 RMB per 1,000 tokens.

WeChat ID

kongyang

Citation

If you use this model in your research, please cite:

@misc{kongyang2023heackmt5zhsum100k,
    title={HeackMT5-ZhSum100k: A Large-Scale Multilingual Abstractive Summarization for Chinese Texts},
    author={Kong Yang},
    year={2023}
}
Downloads last month
113
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.