Information about project you an fine on the website of University of Oslo

For sentence level there are 3 classes:

• negative
• neutral
• positive

For document level we also 3 distinguish classes but with different meaning:

• negative
• fair
• positive

For this run finetune.py and specify required arguments:

• -level: 'sentence' if you want to use corpora with sentence-level sentiment analysys or 'document' for document-level SA. 'other' if you want to use your own corpora.
• -model: pre-traied model from huggingface or absolute (!) path to local folder with config (.json) and model (.bin) in case you want to use custom wrapper.

• -custom_wrapper = True. It's False by default

If you want to use T5 model, please specify:

• -t5 = True. It's False by default.

There are also additional arguments possible but not required:

• -data_path: if you want to use your own corpora provide a path to folder with train.csv, dev.csv and test.csv datasets (column 'sentiment' with labels and 'review' with texts). In this case don't forget to specify -level as 'other'. If you want to use NoReC datasets for sentence or document level SA no need to provide a path: datasets will be downloaded from repo for sentence-level SA or repo for document-level SA depending on the level you specified in the first argument. Repos will only be downloaded once, created dataframes will be stored in 'data/documnent/' and 'data/sentence' and will be used in the future experiments (no need to specify this path, script finds it automatically).
• -lr: 1e-05 by default.
• -max_length: 512 by default.
• -warmup: 2 by default.
• -batch_size: 4 by default.
• -epochs: 10 by default