nickmuchi/deberta-v3-base-finetuned-finance-text-classification
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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
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
