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--- |
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license: cc-by-nc-sa-4.0 |
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language: |
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- zh |
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pipeline_tag: summarization |
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tags: |
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- mT5 |
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- summarization |
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--- |
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# HeackMT5-ZhSum100k: A Summarization Model for Chinese Texts |
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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. |
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## Model Details |
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- Model: mT5 |
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- Language: Chinese |
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- Training data: Mainly Chinese Financial News Sources, NO BBC or CNN source. Training data contains 1M lines. |
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- Finetuning epochs: 10 |
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## Evaluation Results |
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The model achieved the following results: |
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- ROUGE-1: 56.46 |
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- ROUGE-2: 45.81 |
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- ROUGE-L: 52.98 |
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- ROUGE-Lsum: 20.22 |
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## Usage |
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Here is how you can use this model for text summarization: |
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```python |
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from transformers import MT5ForConditionalGeneration, T5Tokenizer |
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model = MT5ForConditionalGeneration.from_pretrained("heack/HeackMT5-ZhSum100k") |
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tokenizer = T5Tokenizer.from_pretrained("heack/HeackMT5-ZhSum100k") |
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chunk = """ |
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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。 |
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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。 |
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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。 |
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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。 |
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""" |
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inputs = tokenizer.encode("summarize: " + chunk, return_tensors='pt', max_length=512, truncation=True) |
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summary_ids = model.generate(inputs, max_length=150, num_beams=4, length_penalty=1.5, no_repeat_ngram_size=2) |
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summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) |
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print(summary) |
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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 |
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``` |
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## 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： |
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```python |
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from transformers import MT5ForConditionalGeneration, T5Tokenizer |
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model_heack = MT5ForConditionalGeneration.from_pretrained("heack/HeackMT5-ZhSum100k") |
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tokenizer_heack = T5Tokenizer.from_pretrained("heack/HeackMT5-ZhSum100k") |
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def _split_text(text, length): |
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chunks = [] |
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start = 0 |
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while start < len(text): |
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if len(text) - start > length: |
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pos_forward = start + length |
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pos_backward = start + length |
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pos = start + length |
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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 {'.', '。','，',','}: |
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pos_forward += 1 |
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pos_backward -= 1 |
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if pos_forward - pos >= 20 and pos_backward <= pos - 20: |
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pos = start + length |
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elif text[pos_backward] in {'.', '。','，',','}: |
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pos = pos_backward |
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else: |
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pos = pos_forward |
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chunks.append(text[start:pos+1]) |
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start = pos + 1 |
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else: |
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chunks.append(text[start:]) |
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break |
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# Combine last chunk with previous one if it's too short |
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if len(chunks) > 1 and len(chunks[-1]) < 100: |
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chunks[-2] += chunks[-1] |
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chunks.pop() |
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return chunks |
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def get_summary_heack(text, each_summary_length=150): |
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chunks = _split_text(text, 300) |
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summaries = [] |
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for chunk in chunks: |
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inputs = tokenizer_heack.encode("summarize: " + chunk, return_tensors='pt', max_length=512, truncation=True) |
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summary_ids = model_heack.generate(inputs, max_length=each_summary_length, num_beams=4, length_penalty=1.5, no_repeat_ngram_size=2) |
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summary = tokenizer_heack.decode(summary_ids[0], skip_special_tokens=True) |
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summaries.append(summary) |
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return " ".join(summaries) |
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``` |
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## Credits |
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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. |
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## License |
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This model is released under the CC BY-NC-SA 4.0 license. |
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并且: |
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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。 |
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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. |
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## WeChat ID |
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kongyang |
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## Citation |
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If you use this model in your research, please cite: |
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```bibtex |
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@misc{kongyang2023heackmt5zhsum100k, |
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title={HeackMT5-ZhSum100k: A Large-Scale Multilingual Abstractive Summarization for Chinese Texts}, |
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author={Kong Yang}, |
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year={2023} |
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} |
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