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- # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
-
- import argparse
- import os
- import time
-
- from utils import load_txt, write_json_file
-
- from paddlenlp import Taskflow
- from paddlenlp.utils.log import logger
-
-
- def main(args):
- """
- Predict based on Taskflow.
- """
- start_time = time.time()
- # read file
- logger.info("Trying to load dataset: {}".format(args.file_path))
- if not os.path.exists(args.file_path):
- raise ValueError("something with wrong for your file_path, it may not exist.")
- examples = load_txt(args.file_path)
-
- # define Taskflow for sentiment analysis
- schema = eval(args.schema)
- if args.load_from_dir:
- senta = Taskflow(
- "sentiment_analysis",
- model=args.model,
- schema=schema,
- aspects=args.aspects,
- batch_size=args.batch_size,
- max_seq_len=args.max_seq_len,
- task_path=args.load_from_dir,
- )
- else:
- senta = Taskflow(
- "sentiment_analysis",
- model=args.model,
- schema=schema,
- aspects=args.aspects,
- batch_size=args.batch_size,
- max_seq_len=args.max_seq_len,
- )
-
- # predict with Taskflow
- logger.info("Start to perform sentiment analysis for your dataset, this may take some time.")
- results = senta(examples)
-
- # save results
- save_path = args.save_path
- if not save_path:
- save_dir = os.path.dirname(args.file_path)
- save_path = os.path.join(save_dir, "sentiment_results.json")
- write_json_file(results, save_path)
- logger.info("The results of sentiment analysis has been saved to: {}".format(save_path))
- logger.info("This run take {} seconds.".format(time.time() - start_time))
-
-
- if __name__ == "__main__":
- # yapf: disable
- parser = argparse.ArgumentParser()
-
- parser.add_argument("--file_path", type=str, default="./data/test_hotel.txt", help="The file path that you want to perform sentiment analysis on.")
- parser.add_argument("--save_path", type=str, default="./data/sentiment_analysis.json", help="The saving path for the results of sentiment analysis.")
- parser.add_argument("--model", choices=['uie-senta-base', 'uie-senta-medium', 'uie-senta-mini', 'uie-senta-micro', 'uie-senta-nano'], default="uie-senta-base", help="The model name that you wanna use for sentiment analysis.")
- parser.add_argument("--load_from_dir", default=None, type=str, help="The directory path for the finetuned model to predict, if set None, it will download model according to model_name.")
- parser.add_argument("--schema", default="[{'评价维度': ['观点词', '情感倾向[正向,负向,未提及]']}]", type=str, help="The schema for UIE to extract infomation.")
- parser.add_argument("--aspects", default=None, type=str, nargs="+", help="A list of pre-given aspects, that is to say, Pipeline only perform sentiment analysis on these pre-given aspects if you input it.")
- parser.add_argument("--batch_size", type=int, default=4, help="Batch size per GPU/CPU for training.")
- parser.add_argument("--max_seq_len", type=int, default=512, help="The maximum total input sequence length after tokenization.")
-
- args = parser.parse_args()
- # yapf: enable
-
- main(args)
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