loadComponentModel(taskFlowPath); ITask task = taskFlowModel.getTask(new TaskFlowBuilder()); ITaskRuntime taskRt = new TaskRuntimeImpl(taskStateStore); taskStepReturn = task.execute(taskRt); 实现NopTaskFlow的第一
wgethttps://bj.bcebos.com/paddlenlp/taskflow/demo/model_config.json-P/home/aistudio/custom_model 5.2 使用定制化模型 通过task_path指定自定义模型路径一键加载即可frompaddlenlpimportTaskflow my_senta=Taskflow("sentiment_analysis",model="skep_ernie_1.0_large_ch",task_path="/home/aistudio/custom_model...
ITask task = taskFlowManager.loadTaskFromPath("/nop/demo/task/discount-01.task.yaml"); ITaskRuntime taskRt = taskFlowManager.newTaskRuntime(task, false, null); BookOrder bookOrder = new BookOrder(); bookOrder.setOriginalPrice(500.0); taskRt.setInput("order", bookOrder); Map<String, Obj...
通过task_path指定自定义模型路径一键加载即可。 frompaddlenlpimportTaskflow my_senta=Taskflow("sentiment_analysis",model="skep_ernie_1.0_large_ch",task_path="/home/aistudio/custom_model") print(my_senta("不错的酒店,服务还可以,下次还会入住的~")) [{‘text’: ‘不错的酒店,服务还可以,下次还会...
python finetune.py --train_data train_data --save_dir ./checkpoint --model uie-base --learning_rate 1e-5 --num_epochs 10 # 使用微调后的模型进行信息抽取 # fine_tuned_ie = Taskflow("information_extraction", schema=schema, task_path="./checkpoint/model_best") # result = fine_tuned_...
task_path:自定义任务路径,默认为 None 。 3. 命名实体识别 最全中文实体标签 精确模式:(默认),基于百度解语,内置91种词性及专名类别标签 In [12] ner = Taskflow("ner") pprint(ner("《孤女》是2010年九州出版社出版的小说,作者是余兼羽")) [2023-06-05 14:21:31,028] [ INFO] - Already cached...
-task = Taskflow("invalid_model")+task = Taskflow("text_classification") 1. 2. 错误日志示例 2023-04-01 12:00:00 ERROR: Model not found at path '/invalid_model_path'. 1. 扩展应用 paddleNLP Taskflow可应用于多个场景,使用需求图和关系图展示: ...
TaskFlowModel taskFlowModel = resourceComponentManager.loadComponentModel(taskFlowPath); ITask task = taskFlowModel.getTask(new TaskFlowBuilder()); ITaskRuntime taskRt = new TaskRuntimeImpl(taskStateStore); taskStepReturn = task.execute(taskRt); ...
("information_extraction", schema=schema, task_path='./checkpoint/model_best') ff = open('result.txt', 'w') for line in open(test_file, 'r',encoding='utf-8'): result_item=json.loads(line) target = predict(ie, result_item) ff.write(json.dumps(target, ensure_ascii=False) + '\...
"task": { "id": 5, // 任务的输入实参 "input": {} }, // 异步处理的话会有这个,主要传递service-flow任务的参数 "submit": { // REST提交时: "host": "", "service": "", "path": "", // Kafka提交时 "servers": "", "topic": "" ...