Update README.md

spectra/readme.md

@@ -4,8 +4,33 @@ pip install -r requirements.txt
 ```
 If you want to use apex for AMP training, please clone the apex source code from the repository at github.com to install.
 
-## Fine-tune
+## Fine-tuning
 We provide the pre-trained checkpoint of our model at [huggingface.co](https://huggingface.co/publicstaticvo/SPECTRA-base). To reproduce our result in the paper, please first download the pre-processed fine-tuning data (be available soon), then run `scripts/finetune.sh`
+### Datasets
+Here are the processed fine-tuning data datasets: 
+[**MOSI**](https://space-mm-data.oss-cn-wulanchabu.aliyuncs.com/downstreamv2/mosi.tgz), 
+[**MOSEI**](https://space-mm-data.oss-cn-wulanchabu.aliyuncs.com/downstreamv2/mosei.tgz), 
+[**IEMOCAP**](https://space-mm-data.oss-cn-wulanchabu.aliyuncs.com/downstreamv2/iemocap.tgz), and 
+[**MINTREC**](https://space-mm-data.oss-cn-wulanchabu.aliyuncs.com/downstreamv2/mintrec.tgz). 
+These are all composed by pickles and can be used directly. 
+
+> Due to the large data size of SpokenWOZ and Spotify-100k (tens of GBs), please obtain from the original repo."
+
+### Usage
+To access the training, validation, and test files in the datasets, you can use the following command to extract the mosi.tgz file:
+
+```
+tar -xzvf mosi.tgz
+```
+
+Once extracted, you'll find .pkl files for training, validation, and testing. Each pickle file contains a list of samples, and each sample includes the following components:
+1. Audio Features: This field contains the audio feature data.
+2. Text Token IDs: Here, you'll find the IDs corresponding to text tokens.
+3. Label: This is the label assigned to the sample.
+4. History Audio Features (if applicable): If present, this field contains historical audio feature data.
+5. History Text Token IDs (if applicable): Similar to the above, this includes historical text token IDs, if available.
+
+We hope this information helps you in utilizing the dataset effectively. Should you have any questions or need further assistance, please feel free to reach out.
 
 ## Pre-Train
 To pretrain our model from scratch, please first download our processed pretraining dataset (be available soon), then download pre-trained WavLM and RoBERTa models from huggingface.co (optional), and run `scripts/train-960.sh`