FINPerceiver is a fine-tuned Perceiver IO language model for financial sentiment analysis. More details on the training process of this model are available on the GitHub repository.
Weights & Biases was used to track experiments.
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
This model was trained on the Financial PhraseBank (>= 50% agreement)
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