FINPerceiver is a fine-tuned Perceiver IO model for financial sentiment analysis.
We achieved the following results with 10-fold cross validation.
eval/accuracy 0.8624 (stdev 0.01922)
eval/f1 0.8416 (stdev 0.03738)
eval/loss 0.4314 (stdev 0.05295)
eval/precision 0.8438 (stdev 0.02938)
eval/recall 0.8415 (stdev 0.04458)
The hyperparameters used are as follows.
per_device_train_batch_size 16
per_device_eval_batch_size 16
num_train_epochs 4
learning_rate 2e-5
Create W&B API token, W&B/HF CLI login, ... (TBD)
pip3 install -r requirements.txt
WANDB_PROJECT=fin_perceiver python train_folds.py
This model was trained on the Financial PhraseBank (>= 50% agreement) from Malo et al. (2014)