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sec-bert-finetuned-finance-classification

This model is a fine-tuned version of nlpaueb/sec-bert-base on the sentence_50Agree financial-phrasebank + Kaggle Dataset, a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). The Kaggle dataset includes Covid-19 sentiment data and can be found here: sentiment-classification-selflabel-dataset.

It achieves the following results on the evaluation set:

  • Loss: 0.5277
  • Accuracy: 0.8755
  • F1: 0.8744
  • Precision: 0.8754
  • Recall: 0.8755

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 8
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
0.6005 0.99 71 0.3702 0.8478 0.8465 0.8491 0.8478
0.3226 1.97 142 0.3172 0.8834 0.8822 0.8861 0.8834
0.2299 2.96 213 0.3313 0.8814 0.8805 0.8821 0.8814
0.1277 3.94 284 0.3925 0.8775 0.8771 0.8770 0.8775
0.0764 4.93 355 0.4517 0.8715 0.8704 0.8717 0.8715
0.0533 5.92 426 0.4851 0.8735 0.8728 0.8731 0.8735
0.0363 6.9 497 0.5107 0.8755 0.8743 0.8757 0.8755
0.0248 7.89 568 0.5277 0.8755 0.8744 0.8754 0.8755

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.4
  • Tokenizers 0.11.6
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Datasets used to train nickmuchi/sec-bert-finetuned-finance-classification

Evaluation results