Hugging Face's logo

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

Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
deberta-v2
Generated from Trainer
financial-sentiment-analysis
sentiment-analysis
sentence_50agree
stocks
sentiment
finance
Inference Endpoints
Model card Files Metrics Community
2
deberta-v3-base-finetuned-finance-text-classification / README.md
nickmuchi's picture
nickmuchi
Update README.md
e77ad9b
preview code
|
raw
history blame
3.85 kB
metadata
license: mit
tags:
  - generated_from_trainer
  - financial-sentiment-analysis
  - sentiment-analysis
  - sentence_50agree
  - financial
  - stocks
  - sentiment
datasets:
  - financial_phrasebank
  - Kaggle_Self_label
  - nickmuchi/financial-classification
widget:
  - text: The USD rallied by 3% last night as the Fed hiked interest rates
    example_title: Bullish Sentiment
  - text: >-
      Covid-19 cases have been increasing over the past few months impacting
      earnings for global firms
    example_title: Bearish Sentiment
  - text: the USD has been trending lower
    example_title: Mildly Bearish Sentiment
  - text: >-
      The USD rallied by 3% last night as the Fed hiked interest rates however,
      higher interest rates will increase mortgage costs for homeowners
    example_title: Neutral
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: deberta-v3-base-finetuned-finance-text-classification
    results: []

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
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
No log 1.0 285 0.4187 0.8399 0.8407 0.8687 0.8399
0.5002 2.0 570 0.3065 0.8755 0.8733 0.8781 0.8755
0.5002 3.0 855 0.4148 0.8775 0.8775 0.8778 0.8775
0.1937 4.0 1140 0.4249 0.8696 0.8699 0.8719 0.8696
0.1937 5.0 1425 0.5121 0.8834 0.8824 0.8831 0.8834
0.0917 6.0 1710 0.6113 0.8775 0.8779 0.8839 0.8775
0.0917 7.0 1995 0.7296 0.8775 0.8776 0.8793 0.8775
0.0473 8.0 2280 0.7034 0.8953 0.8942 0.8964 0.8953
0.0275 9.0 2565 0.6995 0.8834 0.8836 0.8846 0.8834
0.0275 10.0 2850 0.7736 0.8755 0.8755 0.8789 0.8755
0.0186 11.0 3135 0.7173 0.8814 0.8814 0.8840 0.8814
0.0186 12.0 3420 0.7659 0.8854 0.8852 0.8873 0.8854
0.0113 13.0 3705 0.8415 0.8854 0.8855 0.8907 0.8854
0.0113 14.0 3990 0.7577 0.8953 0.8951 0.8966 0.8953
0.0074 15.0 4275 0.7687 0.8913 0.8912 0.8927 0.8913

Framework versions

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