detect multiple emotions in a sentence including [anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, trust]
Project description
Multi-emotion Recognition Using Multi-EmoBERT and Emotion Analysis in Fake News
This is the official PyTorch repo for Multi-EmoBERT, a learning framework for multi-emotion recognition.
Multi-emotion Recognition Using Multi-EmoBERT and Emotion Analysis in Fake News
Jinfen Li, Lu Xiao
WebSci 2023
If Multi-EmoBERT is helpful for your research, please consider citing our paper:
@inproceedings{li2023multi,
title={Multi-emotion Recognition Using Multi-EmoBERT and Emotion Analysis in Fake News},
author={Li, Jinfen and Xiao, Lu},
booktitle={Proceedings of the 15th ACM Web Science Conference 2023},
pages={128--135},
year={2023}
}
Basics
Resources
create a folder named "resources" and put the following resources here Stanford CoreNLP
NRC Emotion Lexicon v0.2: we use NRC-Emotion-Lexicon-Wordlevel-v0.2.txt and rename it as NRC-Emotion-Lexicon.txt
Environment
create a virtual environment
conda create -n emo_env python=3.9.16
install packages via conda first and then via pip
conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit
conda install -c anaconda cudnn
conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia
conda install openjdk=8
pip install -r requirements.txt
rename .env.example as .env and change the variable values in the file
Multirun
Do grid search over different configs.
python main.py -m \
dataset=se_english \
seed=0,1,2,3,4,5 \
Evaluate checkpoint
This command evaluates a checkpoint on the train, dev, and test sets.
python main.py \
training=evaluate \
training.ckpt_path=/path/to/ckpt \
training.eval_splits=train,dev,test \
Finetune checkpoint
python main.py \
training=evaluate \
training.ckpt_path=/path/to/ckpt \
Offline Mode
In offline mode, results are not logged to Neptune.
python main.py logger.offline=True
Debug Mode
In debug mode, results are not logged to Neptune, and we only train/evaluate for limited number of batches and/or epochs.
python main.py debug=True
Hydra Working Directory
Hydra will change the working directory to the path specified in configs/hydra/default.yaml
. Therefore, if you save a file to the path './file.txt'
, it will actually save the file to somewhere like logs/runs/xxxx/file.txt
. This is helpful when you want to version control your saved files, but not if you want to save to a global directory. There are two methods to get the "actual" working directory:
- Use
hydra.utils.get_original_cwd
function call - Use
cfg.work_dir
. To use this in the config, can do something like"${data_dir}/${.dataset}/${model.arch}/"
Config Key
-
work_dir
current working directory (wheresrc/
is) -
data_dir
where data folder is -
log_dir
where log folder is (runs & multirun) -
root_dir
where the saved ckpt & hydra config are
Example Commands
Here, we assume the following:
- The
data_dir
isdata
, which meansdata_dir=${work_dir}/../data
. - The dataset is
semEval 2018 task 1-english
.
1. Build dataset
The commands below are used to build pre-processed datasets, saved as pickle files. The model architecture is specified so that we can use the correct tokenizer for pre-processing. Remember to put a xxx.yaml file in the configs/dataset folder for the dataset you want to build.
python scripts/build_dataset.py --data_dir data \
--dataset se_english --arch bert-base-uncased --split train
python scripts/build_dataset.py --data_dir data \
--dataset se_english --arch bert-base-uncased --split dev
python scripts/build_dataset.py --data_dir data \
--dataset se_english --arch bert-base-uncased --split test
If the dataset is very large, you have the option to subsample part of the dataset for smaller-scale experiements. For example, in the command below, we build a train set with only 1000 train examples (sampled with seed 0).
python scripts/build_dataset.py --data_dir data \
--dataset se_english --arch bert-base-uncased --split train \
--num_samples 1000 --seed 0
2. Train Multi-EmoBERT
The command below is the most basic way to run main.py
python main.py -m \
data=se_english \
model=lm \
model.optimizer.lr=2e-5 \
setup.train_batch_size=32 \
setup.accumulate_grad_batches=1 \
setup.eff_train_batch_size=32 \
setup.eval_batch_size=32 \
setup.num_workers=3 \
seed=0,1,2
3. Train Model with Hashtag Encoding, Sentiment Composition and Emotion Correlation
This repo implements a number of different methods for training the Task LM. Below are commands for running each method.
Task LM + Hashtag Encoding
python main.py -m \
data=se_english \
model=lm \
model.use_hashtag=True \
model.hashtag_emb_dim=80 \
model.optimizer.lr=2e-5 \
setup.train_batch_size=32 \
setup.accumulate_grad_batches=1 \
setup.eff_train_batch_size=32 \
setup.eval_batch_size=32 \
setup.num_workers=3 \
seed=0,1,2
Task LM + Sentiment Composition
python main.py -m \
data=se_english \
model=lm \
model.use_senti_tree=True \
model.phrase_emb_dim=80 \
model.optimizer.lr=2e-5 \
setup.train_batch_size=32 \
setup.accumulate_grad_batches=1 \
setup.eff_train_batch_size=32 \
setup.eval_batch_size=32 \
setup.num_workers=3 \
seed=0,1,2
Task LM + Emotion Correlation
python main.py -m \
data=se_english \
model=lm \
model.use_emo_cor=True \
model.optimizer.lr=2e-5 \
setup.train_batch_size=32 \
setup.accumulate_grad_batches=1 \
setup.eff_train_batch_size=32 \
setup.eval_batch_size=32 \
setup.num_workers=3 \
seed=0,1,2
4. Evaluate Model
exp_id is the folder name under your save_dir (e.g., "se_english_bert-base-uncased_use-hashtag-True_use-senti-tree-True_xxx"), ckpt_path is the checkpoint under the checkpoints folder in the exp_id folder. The results will be saved in the model_outputs folder in the exp_id folder.
python main.py -m \
data=se_english \
training=evaluate \
ckpt_path = xxx \
exp_id = xxx \
setup.train_batch_size=32 \
setup.accumulate_grad_batches=1 \
setup.eff_train_batch_size=32 \
setup.eval_batch_size=32 \
setup.num_workers=3 \
seed=0,1,2
5. Predict Your Own Data
The pip installable package is available soon ...
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file multi_emotion_recognition-0.1.2.tar.gz
.
File metadata
- Download URL: multi_emotion_recognition-0.1.2.tar.gz
- Upload date:
- Size: 18.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.16
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | fb64e2e229542afa5ea37b39988e0b6b8e94de7f68365e39cf600f66856764f4 |
|
MD5 | bfca8a596daf7a18ac2a0aa396d73a5b |
|
BLAKE2b-256 | f901f5c14e70d47b3f265bc8f5242591dedfdb2af8b29964bda0ecfcda98a733 |