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--- |
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library_name: transformers |
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base_model: bert-base-chinese |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cmrc2018 |
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model-index: |
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- name: chinese_qa |
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results: [] |
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--- |
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# bert-base-chinese-finetuned-cmrc2018 |
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This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the CMRC2018 (Chinese Machine Reading Comprehension) dataset. |
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## Model Description |
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This is a BERT-based extractive question answering model for Chinese text. The model is designed to locate and extract answer spans from given contexts in response to questions. |
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Key Features: |
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- Base Model: bert-base-chinese |
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- Task: Extractive Question Answering |
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- Language: Chinese |
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- Training Dataset: CMRC2018 |
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## Performance Metrics |
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Evaluation results on the test set: |
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- Exact Match: 59.708 |
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- F1 Score: 60.0723 |
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- Number of evaluation samples: 6,254 |
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- Evaluation speed: 283.054 samples/second |
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## Intended Uses & Limitations |
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### Intended Uses |
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- Chinese reading comprehension tasks |
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- Answer extraction from given documents |
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- Context-based question answering systems |
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### Limitations |
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- Only supports extractive QA (cannot generate new answers) |
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- Answers must be present in the context |
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- Does not support multi-hop reasoning |
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- Cannot handle unanswerable questions |
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## Training Details |
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### Training Hyperparameters |
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- Learning rate: 3e-05 |
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- Train batch size: 12 |
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- Eval batch size: 8 |
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- Seed: 42 |
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- Optimizer: AdamW (betas=(0.9,0.999), epsilon=1e-08) |
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- LR scheduler: linear |
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- Number of epochs: 5.0 |
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### Training Results |
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- Training time: 892.86 seconds |
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- Training samples: 18,960 |
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- Training speed: 106.175 samples/second |
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- Training loss: 0.5625 |
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### Framework Versions |
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- Transformers: 4.47.0.dev0 |
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- Pytorch: 2.5.1+cu124 |
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- Datasets: 3.1.0 |
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- Tokenizers: 20.3 |
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## Usage |
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```python |
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import torch |
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer |
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# Load model and tokenizer |
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model = AutoModelForQuestionAnswering.from_pretrained("real-jiakai/bert-base-chinese-finetuned-cmrc2018") |
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tokenizer = AutoTokenizer.from_pretrained("real-jiakai/bert-base-chinese-finetuned-cmrc2018") |
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# Prepare inputs |
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question = "长城有多长？" |
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context = "长城是中国古代的伟大建筑工程，全长超过2万公里，横跨中国北部多个省份。" |
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# Tokenize inputs |
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inputs = tokenizer( |
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question, |
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context, |
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return_tensors="pt", |
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max_length=384, |
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truncation=True |
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) |
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# Get answer |
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outputs = model(**inputs) |
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answer_start = torch.argmax(outputs.start_logits) |
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answer_end = torch.argmax(outputs.end_logits) + 1 |
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answer = tokenizer.decode(inputs["input_ids"][0][answer_start:answer_end]) |
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print("Answer:", answer) |
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``` |
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## Citation |
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If you use this model, please cite the CMRC2018 dataset: |
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```bibtex |
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@inproceedings{cui-emnlp2019-cmrc2018, |
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title = "A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension", |
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author = "Cui, Yiming and |
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Liu, Ting and |
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Che, Wanxiang and |
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Xiao, Li and |
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Chen, Zhipeng and |
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Ma, Wentao and |
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Wang, Shijin and |
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Hu, Guoping", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", |
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month = nov, |
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year = "2019", |
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address = "Hong Kong, China", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/D19-1600", |
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doi = "10.18653/v1/D19-1600", |
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pages = "5886--5891", |
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} |
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``` |
