Skip to content
Open
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -116,13 +116,13 @@ We welcome new contributions to MultiBench through new research areas, datasets,

We release the processed datasets: [sarcasm](https://drive.google.com/drive/folders/1JFcX-NF97zu9ZOZGALGU9kp8dwkP7aJ7?usp=sharing), [mosi](https://drive.google.com/drive/folders/1uEK737LXB9jAlf9kyqRs6B9N6cDncodq?usp=sharing), [mosei](https://drive.google.com/drive/folders/1A_hTmifi824gypelGobgl2M-5Rw9VWHv?usp=sharing), [humor](https://drive.google.com/drive/folders/1Agzm157lciMONHOHemHRSySmjn1ahHX1?usp=sharing). The original datasets are also publicly available at [MultimodalSDK](https://github.com/matsuolab/CMU-MultimodalSDK) for MOSI and MOSEI, [MUsTARD](https://github.com/soujanyaporia/MUStARD) and [UR-Funny](https://github.com/ROC-HCI/UR-FUNNY). You can obtain processed data with `datasets/affect/get_data.py`, note that `sarcasm` means [MUsTARD](https://github.com/soujanyaporia/MUStARD) and `humor` means [UR-FUNNY](https://github.com/ROC-HCI/UR-FUNNY).

There are several example scripts for running affect datasets under examples/affect/. For example, to run affect datasets with simple late fusion, fistly, you can use
There are several example scripts for running affect datasets under examples/affect/. For example, to run affect datasets with simple late fusion, firstly, you can use

```
traindata, validdata, test_robust = get_dataloader('/home/pliang/multibench/affect/pack/mosi/mosi_raw.pkl', data_type='mosi')
```

or if you don't want to use packed data, and expect data with the same max squence length, use `max_pad` and `max_seq_len` options, and remember to set `is_packed=False` in the `train` and `test` functions
or if you don't want to use packed data, and expect data with the same max sequence length, use `max_pad` and `max_seq_len` options, and remember to set `is_packed=False` in the `train` and `test` functions

```
traindata, validdata, testdata = get_dataloader('/home/pliang/multibench/affect/pack/mosi/mosi_raw.pkl', data_type='mosi', max_pad=True, max_seq_len=50)
Expand Down