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nickmuchi/deberta-v3-base-finetuned-finance-text-classification

sourceHugging Facemitupdated 4y agoView on Hugging Face
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deberta-v3-base-finetuned-finance-text-classification

This model is a fine-tuned version of microsoft/deberta-v3-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: 0.7687
  • Accuracy: 0.8913
  • F1: 0.8912
  • Precision: 0.8927
  • Recall: 0.8913

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
  • trainbatchsize: 16
  • evalbatchsize: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lrschedulertype: linear
  • num_epochs: 15
  • mixedprecisiontraining: Native AMP

Training results

Training LossEpochStepValidation LossAccuracyF1PrecisionRecall
No log1.02850.41870.83990.84070.86870.8399
0.50022.05700.30650.87550.87330.87810.8755
0.50023.08550.41480.87750.87750.87780.8775
0.19374.011400.42490.86960.86990.87190.8696
0.19375.014250.51210.88340.88240.88310.8834
0.09176.017100.61130.87750.87790.88390.8775
0.09177.019950.72960.87750.87760.87930.8775
0.04738.022800.70340.89530.89420.89640.8953
0.02759.025650.69950.88340.88360.88460.8834
0.027510.028500.77360.87550.87550.87890.8755
0.018611.031350.71730.88140.88140.88400.8814
0.018612.034200.76590.88540.88520.88730.8854
0.011313.037050.84150.88540.88550.89070.8854
0.011314.039900.75770.89530.89510.89660.8953
0.007415.042750.76870.89130.89120.89270.8913

Framework versions

  • Transformers 4.19.2
  • Pytorch 1.11.0+cu113
  • Datasets 2.2.2
  • Tokenizers 0.12.1