Multidimensional sentiment analysis tasks

Our task is to determine the emotional tendency of each restaurant review text in 18 dimensions. These 18 dimensions are:

• Location#Transportation
• Location#Downtown
• Location#Easy_to_find
• Service#Queue
• Service#Hospitality
• Service#Parking
• Service#Timely
• Price#Level
• Price#Cost_effective
• Price#Discount
• Ambience#Decoration
• Ambience#Noise
• Ambience#Space
• Ambience#Sanitary
• Food#Portion
• Food#Taste
• Food#Appearance

If you want to know more about the dataset and metrics, see https://github.com/Meituan-Dianping/asap

Environment setup & qlora fine-tuning

My device：Linux，pytorch2.0.1+cu118, A100

1. Before starting, please install the xtuner library first：

mkdir -p /root/xtuner0117 && cd /root/xtuner0117

# Pull the source code of version 0.1.17
git clone -b v0.1.17  https://github.com/InternLM/xtuner

# Users who cannot access github please pull from gitee:
# git clone -b v0.1.15 https://gitee.com/Internlm/xtuner

# Enter the source code directory
cd /root/xtuner0117/xtuner

# Install XTuner from source
pip install -e '.[all]'

2. With the support of the xtuner library, execute the following fine-tuning instructions to merge the qlora and qwen model configuration files：

xtuner train qwen_1.8B_qlora_ASCA.py --deepspeed deepspeed_zero2 # Add deepspeed to accelerate training

xtuner convert pth_to_hf qwen_1.8B_qlora_ASCA.py
./work_dirs/qwen_1.8B_qlora_ASCA/iter_1803.pth ./hf

# Merge qlora files to generate fine-tuned qwen model
xtuner convert merge ./qwen/Qwen1.5-1.8B ./hf Qwen-1.5-1.8B-ASCA --max-shard-size 2GB

# Remove intermediate products
rm -rf ./hf

Inference

If you want to perform inference, you only need to execute python main.py in the system terminal in the directory where the current folder is located. If you configure the relevant environment correctly, you will run successfully. The average accuracy of all test sets in 18 dimensions reached 86.1%.