Training procedure

Framework versions

• SWIFT 1.5.3

Base model information

• BaseModel Class QWenLMHeadModel

情感分析（作业）

SentimentanalysisHomework

• 简介：

• 情感分析模型，以问答形式对用户输入内容的情感倾向进行二分类

• 实验环境：

• Ubuntu22.04-cuda12.1.0-py310-torch2.1.2-tf2.14.0-1.11.0

• 8核32G A10 24G显存

• 训练方法：

• 模型：qwen1_8b

• 数据集：jdsentimentzh

• 超参数：

  sft_type='lora',
  train_dataset_sample=2000,

• 示例代码：

  import os
  os.environ['CUDA_VISIBLE_DEVICES'] = '0'
  import torch
  from swift.llm import (
  DatasetName, InferArguments, ModelType, SftArguments,
  infer_main, sft_main, app_ui_main, merge_lora_main
  )
  model_type = ModelType.qwen_1_8b
  sft_args = SftArguments(
  model_type=model_type,
  sft_type='lora',
  train_dataset_sample=2000,
  dataset=[DatasetName.jd_sentiment_zh],
  output_dir='output')
  result = sft_main(sft_args)
  best_model_checkpoint = result['best_model_checkpoint']
  print(f'best_model_checkpoint: {best_model_checkpoint}')
  torch.cuda.empty_cache()
  infer_args = InferArguments(
  ckpt_dir=best_model_checkpoint,
  load_dataset_config=True,
  do_sample=False)
  result = infer_main(infer_args)

• 推理效果：

  Sentence: 擦玻璃很好、就是太小了
  Category: negative, positive
  Output:[OUTPUT]positive<|endoftext|>

  [LABELS]positive
  --------------------------------------------------
  [PROMPT]Task: Sentiment Classification
  Sentence: 店家太不负责任了，衣服质量太差劲了，和图片上的不一样
  Category: negative, positive
  Output:[OUTPUT]negative<|endoftext|>

  [LABELS]negative
  --------------------------------------------------
  [PROMPT]Task: Sentiment Classification
  Sentence: 送国际友人挺好的，不错不错！
  Category: negative, positive
  Output:[OUTPUT]positive<|endoftext|>

  [LABELS]positive
  --------------------------------------------------
  [PROMPT]Task: Sentiment Classification
  Sentence: 很好,装好一定很漂亮
  Category: negative, positive
  Output:[OUTPUT]positive<|endoftext|>

  [LABELS]positive
  --------------------------------------------------
  [PROMPT]Task: Sentiment Classification
  Sentence: 东西给你退回去了，你要黑我钱！！！
  Category: negative, positive
  Output:[OUTPUT]negative<|endoftext|>

  [LABELS]negative
  --------------------------------------------------
  [PROMPT]Task: Sentiment Classification
  Sentence: 送货很快，书是正品，买书一直京东是首选！
  Category: negative, positive
  Output:[OUTPUT]positive<|endoftext|>

  [LABELS]positive
  --------------------------------------------------
  [PROMPT]Task: Sentiment Classification
  Sentence: 口感相当的好 都想买第二次了
  Category: negative, positive
  Output:[OUTPUT]positive<|endoftext|>

  [LABELS]positive
  --------------------------------------------------
  [PROMPT]Task: Sentiment Classification
  Sentence: 硅胶味道太重，样子与图片差距太大
  Category: negative, positive
  Output:[OUTPUT]negative<|endoftext|>

  [LABELS]negative
  --------------------------------------------------
  [PROMPT]Task: Sentiment Classification
  Sentence: 很伤心，买了放到三星n4尽然不能用，客服各种推
  Category: negative, positive
  Output:[OUTPUT]negative<|endoftext|>

  [LABELS]negative
  --------------------------------------------------
  [PROMPT]Task: Sentiment Classification
  Sentence: 质量不错，大小合适，应当是正品！但是我买的是黑灰，发来的却是纯黑，懒得换了，给个差评，希望以后改进！
  Category: negative, positive
  Output:[OUTPUT]positive<|endoftext|>

  [LABELS]negative