- Dataset and codes for our IJCAI 2019 paper "Adapting BERT for Target-Oriented Multimodal Sentiment Classification" https://www.ijcai.org/proceedings/2019/0751.pdf
Author
Jianfei YU
Mar 02, 2020
Target-oriented Multimodal Sentiment Classification (TMSC), PyTorch Implementations.
- PyTorch 1.0.0
- Python 3.7
- Step 1: Download each tweet's associated image via this link (https://drive.google.com/file/d/1PpvvncnQkgDNeBMKVgG2zFYuRhbL873g/view)
- Step 2: Change the image path in line 553 and line 555 of the "run_multimodal_classifier.py" file
- Step 3: Download the pre-trained ResNet-152 via this link (https://download.pytorch.org/models/resnet152-b121ed2d.pth)
- Setp 4: Put the pre-trained ResNet-152 model under the folder named "resnet"
- This is optional, because I have provided the pre-processed data under the folder named "absa_data"
python process_absa_data.py- This is the training code of tuning parameters on the dev set, and testing on the test set. Note that you can change "CUDA_VISIBLE_DEVICES=6" based on your available GPUs.
sh run_multimodal_classifier.sh- After training the model, the following code is used for directly loading the trained model and testing it on the test set
sh run_multimodal_classifier_test.shBERT and BERT+BL (run_classifier.py)
- You can run the following code to perform training and testing.
sh run_classifier.shTomBERT, mBERT, Res-BERT (run_multimodal_classifier.py)
- You can choose different models in the "run_multimodal_classifier.sh" file.
- You can download the BERT and TomBERT models trained by me. You can find the results we report in our paper from the "eval_result" files. https://drive.google.com/open?id=1e3rL3G1ojaDWZnrkmZX-uLudPbQo7tVe
- Most of the codes are based on the codes provided by huggingface: https://github.com/huggingface/transformers.