FINPerceiver
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
Datasets
This model was trained on the Financial PhraseBank (>= 50% agreement)
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Dataset used to train warwickai/fin-perceiver
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Evaluation results
- Accuracy on financial_phrasebankself-reported0.862
- F1 on financial_phrasebankself-reported0.842
- Precision on financial_phrasebankself-reported0.844
- Recall on financial_phrasebankself-reported0.842