WRIME-fine-tuned BERT base Japanese

This model is a Japanese BERTBASE fine-tuned on the WRIME dataset. It was trained as part of the paper "Emotion Analysis of Writers and Readers of Japanese Tweets on Vaccinations". Fine-tuning code is available at this repo.

Intended uses and limitations

This model can be used to predict intensities scores for eight emotions for writers and readers. Please refer to the Fine-tuning data section for the list of emotions.

Because of the regression fine-tuning task, it is possible for the model to infer scores outside of the range of the scores of the fine-tuning data (score < 0 or score > 4).

Model Architecture, Tokenization, and Pretraining

The Japanese BERTBASE fine-tuned was cl-tohoku/bert-base-japanese-v2. Please refer to their model card for details regarding the model architecture, tokenization, pretraining data, and pretraining procedure.

Fine-tuning data

The model is fine-tuned on WRIME, a dataset of Japanese Tweets annotated with writer and reader emotion intensities. We use version 1 of the dataset. Each Tweet is accompanied by a set of writer emotion intensities (from the author of the Tweet) and three sets of reader emotions (from three annotators). The emotions follow Plutchhik's emotions, namely:

• joy
• sadness
• anticipation
• surprise
• anger
• fear
• disgust
• trust

These emotion intensities follow a four-point scale:

Fine-tuning

The BERT is fine-tuned to directly regress the emotion intensities of the writer and the averaged emotions of the readers from each Tweet, meaning there are 16 outputs (8 emotions per writer/reader).

The fine-tuning was inspired by common BERT fine-tuning procedures. The BERT was fine-tuned on WRIME for 3 epochs using the AdamW optimizer with a learning rate of 2e-5, β1=0.9, β2=0.999, weight decay of 0.01, linear decay, a warmup ratio of 0.01, and a batch size of 32. Training was conducted with an NVIDIA Tesla K80 and finished in 3 hours.

Evaluation results

Below are the MSEs of the BERT on the test split of WRIME.