ai 分类

一键UIE预测

PaddleNLP提供一键预测功能，无需训练，直接输入数据即可开放域抽取结果。这里以信息抽取-命名实体识别任务，UIE模型为例：

>>> from pprint import pprint
>>> from paddlenlp import Taskflow

>>> schema = ['时间', '选手', '赛事名称'] # Define the schema for entity extraction
>>> ie = Taskflow('information_extraction', schema=schema)
>>> pprint(ie("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！"))
[{'时间': [{'end': 6,
          'probability': 0.9857378532924486,
          'start': 0,
          'text': '2月8日上午'}],
  '赛事名称': [{'end': 23,
            'probability': 0.8503089953268272,
            'start': 6,
            'text': '北京冬奥会自由式滑雪女子大跳台决赛'}],
  '选手': [{'end': 31,
          'probability': 0.8981548639781138,
          'start': 28,
          'text': '谷爱凌'}]}]

服务化部署

在 UTC 的服务化能力中我们提供基于PaddleNLP SimpleServing 来搭建服务化能力，通过几行代码即可搭建服务化部署能力。

# Save at server.py
from paddlenlp import SimpleServer, Taskflow

schema = ["病情诊断", "治疗方案", "病因分析", "指标解读", "就医建议"]
utc = Taskflow("zero_shot_text_classification",
               model="utc-base",
               schema=schema,
               task_path="../../checkpoint/model_best/plm",
               precision="fp32")
app = SimpleServer()
app.register_taskflow("taskflow/utc", utc)

# Start the server
paddlenlp server server:app --host 0.0.0.0 --port 8990




from paddlenlp import SimpleServer, Taskflow

# The schema changed to your defined schema
schema = ["病情诊断", "治疗方案", "病因分析", "指标解读", "就医建议", "疾病表述", "后果表述", "注意事项", "功效作用", "医疗费用", "其他"]
# The task path changed to your best model path
utc = Taskflow(
    "zero_shot_text_classification", model="utc-base", task_path="../../checkpoint/model_best/plm", schema=schema
)
# If you want to define the finetuned utc service
app = SimpleServer()
app.register_taskflow("taskflow/utc", utc)