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metadata
license: cc-by-sa-4.0
tags:
  - financial-sentiment-analysis
  - sentiment-analysis
  - sentence_50agree
  - generated_from_trainer
  - financial
  - stocks
  - sentiment
datasets:
  - financial_phrasebank
  - Kaggle_Self_label
  - nickmuchi/financial-classification
metrics:
  - accuracy
  - f1
  - precision
  - recall
widget:
  - text: The USD rallied by 10% last night
    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
model-index:
  - name: sec-bert-finetuned-finance-classification
    results: []

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