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metadata
tags:
  - generated_from_trainer
  - financial-tweets-sentiment-analysis
  - sentiment-analysis
  - generated_from_trainer
  - financial
  - stocks
  - sentiment
datasets:
  - zeroshot/twitter-financial-news-sentiment
metrics:
  - accuracy
  - f1
  - precision
  - recall
widget:
  - text: $LOW - Lowe's racks up another positive rating despite recession risk
    example_title: Bullish Sentiment
  - text: $HNHAF $HNHPD $AAPL - Trendforce cuts iPhone estimate after Foxconn delay
    example_title: Bearish Sentiment
  - text: >-
      Coin Toss: Morgan Stanley Raises Tesla Bull Case To $500, Keeps Bear Case
      At $10
    example_title: Neutral Sentiment
model-index:
  - name: finbert-tone-finetuned-fintwitter-classification
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: twitter-financial-news-sentiment
          type: finance
        metrics:
          - type: F1
            name: F1
            value: 0.8838
          - type: accuracy
            name: accuracy
            value: 0.884

finbert-tone-finetuned-fintwitter-classification

This model is a fine-tuned version of yiyanghkust/finbert-tone on Twitter Financial News dataset. It achieves the following results on the evaluation set:

  • Loss: 1.4078
  • Accuracy: 0.8840
  • F1: 0.8838
  • Precision: 0.8838
  • Recall: 0.8840

Model description

Model determines the financial sentiment of given tweets. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance..

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: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
0.6385 1.0 597 0.3688 0.8668 0.8693 0.8744 0.8668
0.3044 2.0 1194 0.3994 0.8744 0.8726 0.8739 0.8744
0.1833 3.0 1791 0.6212 0.8781 0.8764 0.8762 0.8781
0.1189 4.0 2388 0.8370 0.8740 0.8743 0.8748 0.8740
0.0759 5.0 2985 0.9107 0.8807 0.8798 0.8796 0.8807
0.0291 6.0 3582 0.9711 0.8836 0.8825 0.8821 0.8836
0.0314 7.0 4179 1.1305 0.8819 0.8811 0.8812 0.8819
0.0217 8.0 4776 1.0190 0.8811 0.8813 0.8816 0.8811
0.0227 9.0 5373 1.1940 0.8844 0.8832 0.8838 0.8844
0.0156 10.0 5970 1.2595 0.8752 0.8768 0.8801 0.8752
0.0135 11.0 6567 1.1931 0.8760 0.8768 0.8780 0.8760
0.009 12.0 7164 1.2154 0.8857 0.8852 0.8848 0.8857
0.0058 13.0 7761 1.3874 0.8748 0.8759 0.8776 0.8748
0.009 14.0 8358 1.4193 0.8740 0.8754 0.8780 0.8740
0.0042 15.0 8955 1.2999 0.8807 0.8800 0.8796 0.8807
0.0028 16.0 9552 1.3428 0.8802 0.8805 0.8817 0.8802
0.0029 17.0 10149 1.3959 0.8807 0.8807 0.8810 0.8807
0.0022 18.0 10746 1.4149 0.8827 0.8823 0.8824 0.8827
0.0037 19.0 11343 1.4078 0.8840 0.8838 0.8838 0.8840
0.001 20.0 11940 1.4236 0.8823 0.8823 0.8825 0.8823

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.0+cu116
  • Datasets 2.8.0
  • Tokenizers 0.13.2