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--- |
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license: apache-2.0 |
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language: zh |
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--- |
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# uie-micro |
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## 介绍 |
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* **[PaddlePaddle/uie-micro](https://huggingface.co/PaddlePaddle/uie-medium)** 的 Pytorch 实现 |
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## 代码调用 |
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### forward |
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#### *Parameters* |
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* `input_ids: Optional[torch.Tensor] = None` |
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* `token_type_ids: Optional[torch.Tensor] = None` |
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* `position_ids: Optional[torch.Tensor] = None` |
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* `attention_mask: Optional[torch.Tensor] = None` |
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* `head_mask: Optional[torch.Tensor] = None` |
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* `inputs_embeds: Optional[torch.Tensor] = None` |
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* `start_positions: Optional[torch.Tensor] = None` |
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* `end_positions: Optional[torch.Tensor] = None` |
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* `output_attentions: Optional[bool] = None` |
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* `output_hidden_states: Optional[bool] = None` |
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* `return_dict: Optional[bool] = None` |
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#### *Returns* |
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* `UIEModelOutput or tuple(torch.FloatTensor)` |
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### predict |
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#### *Parameters* |
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* `schema: Union[Dict, List[str], str]` |
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* `input_texts: Union[List[str], str]` |
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* `tokenizer: PreTrainedTokenizerFast` |
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* `max_length: int = 512` |
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* `batch_size: int = 32` |
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* `position_prob: int = 0.5` |
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* `progress_hook=None` |
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#### *Returns* |
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* `List[Dict]` |
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```python |
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from tqdm import tqdm |
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from transformers import AutoModel, AutoTokenizer |
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model = AutoModel.from_pretrained('Casually/uie-micro', trust_remote_code=True) |
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model.eval().to('cuda') |
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tokenizer = AutoTokenizer.from_pretrained('Casually/uie-micro') |
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hook = tqdm() |
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schema = {'地震触发词': ['地震强度', '时间', '震中位置', '震源深度']} |
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model.predict(schema=schema, |
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input_texts='中国地震台网正式测定：5月16日06时08分在云南临沧市凤庆县(北纬24.34度，东经99.98度)发生3.5级地震，震源深度10千米。', |
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tokenizer=tokenizer, |
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progress_hook=hook |
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) |
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``` |
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```ipython |
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100%|██████████| 5/5 [00:00<00:00, 10.35it/s] |
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[{'地震触发词': [{'end': 58, |
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'probability': 0.9953331562546808, |
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'relations': {'地震强度': [{'end': 56, |
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'probability': 0.9719095188981299, |
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'start': 52, |
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'text': '3.5级'}], |
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'时间': [{'end': 22, |
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'probability': 0.9653931540843175, |
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'start': 11, |
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'text': '5月16日06时08分'}], |
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'震中位置': [{'end': 50, |
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'probability': 0.6063880101553423, |
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'start': 23, |
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'text': '云南临沧市凤庆县(北纬24.34度，东经99.98度)'}], |
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'震源深度': [{'end': 67, |
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'probability': 0.989598549365315, |
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'start': 63, |
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'text': '10千米'}]}, |
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'start': 56, |
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'text': '地震'}]}] |
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``` |
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## 应用示例 |
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### 实体抽取 |
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```python |
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from transformers import AutoModel, AutoTokenizer |
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model = AutoModel.from_pretrained('Casually/uie-micro', trust_remote_code=True) |
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model.eval().to('cuda') |
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tokenizer = AutoTokenizer.from_pretrained('Casually/uie-micro') |
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schema = ['时间', '选手', '赛事名称'] |
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res = model.predict(schema=schema, |
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input_texts="2月8日上午北京冬奥会自由式滑雪女子大跳台决赛中中国选手谷爱凌以188.25分获得金牌！", |
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tokenizer=tokenizer, |
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) |
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``` |
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```ipython |
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>>> from pprint import pprint |
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>>> pprint(res) |
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[{'时间': [{'end': 6, |
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'probability': 0.906374348561485, |
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'start': 0, |
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'text': '2月8日上午'}], |
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'选手': [{'end': 31, |
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'probability': 0.8768158783169611, |
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'start': 28, |
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'text': '谷爱凌'}]}] |
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``` |
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### 关系抽取 |
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```python |
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from transformers import AutoModel, AutoTokenizer |
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model = AutoModel.from_pretrained('Casually/uie-micro', trust_remote_code=True) |
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model.eval().to('cuda') |
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tokenizer = AutoTokenizer.from_pretrained('Casually/uie-micro') |
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schema = {'竞赛名称': ['主办方', '承办方', '已举办次数']} |
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res = model.predict(schema=schema, |
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input_texts='2022语言与智能技术竞赛由中国中文信息学会和中国计算机学会联合主办，百度公司、中国中文信息学会评测工作委员会和中国计算机学会自然语言处理专委会承办，已连续举办4届，成为全球最热门的中文NLP赛事之一。', |
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tokenizer=tokenizer, |
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) |
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``` |
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```ipython |
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>>> from pprint import pprint |
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>>> pprint(res) |
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[{'竞赛名称': [{'end': 13, |
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'probability': 0.6710958346658344, |
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'relations': {'已举办次数': [{'end': 82, |
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'probability': 0.7727802061877256, |
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'start': 80, |
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'text': '4届'}]}, |
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'start': 0, |
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'text': '2022语言与智能技术竞赛'}]}] |
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``` |
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### 事件抽取 |
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```python |
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from transformers import AutoModel, AutoTokenizer |
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model = AutoModel.from_pretrained('Casually/uie-micro', trust_remote_code=True) |
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model.eval().to('cuda') |
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tokenizer = AutoTokenizer.from_pretrained('Casually/uie-micro') |
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schema = {'地震触发词': ['地震强度', '时间', '震中位置', '震源深度']} |
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res = model.predict(schema=schema, |
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input_texts='中国地震台网正式测定：5月16日06时08分在云南临沧市凤庆县(北纬24.34度，东经99.98度)发生3.5级地震，震源深度10千米。', |
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tokenizer=tokenizer, |
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) |
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``` |
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```ipython |
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>>> from pprint import pprint |
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>>> pprint(res) |
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[{'地震触发词': [{'end': 58, |
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'probability': 0.9953331562546808, |
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'relations': {'地震强度': [{'end': 56, |
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'probability': 0.9719095188981299, |
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'start': 52, |
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'text': '3.5级'}], |
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'时间': [{'end': 22, |
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'probability': 0.9653931540843175, |
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'start': 11, |
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'text': '5月16日06时08分'}], |
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'震中位置': [{'end': 50, |
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'probability': 0.6063880101553423, |
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'start': 23, |
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'text': '云南临沧市凤庆县(北纬24.34度，东经99.98度)'}], |
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'震源深度': [{'end': 67, |
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'probability': 0.989598549365315, |
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'start': 63, |
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'text': '10千米'}]}, |
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'start': 56, |
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'text': '地震'}]}] |
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``` |
