readme: ---
  license: cc-by-sa-4.0
  tags:
  - financial-sentiment-analysis
  - sentiment-analysis
  - sentence_50agree
  - generated_from_trainer
  - sentiment
  - finance
  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:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: financial_phrasebank
        type: finance
        args: sentence_50agree
      metrics:
      - type: F1
        name: F1
        value: 0.8744
      - type: accuracy
        name: accuracy
        value: 0.8755
  language:
  - en
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # sec-bert-finetuned-finance-classification
  
  This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/nlpaueb/sec-bert-base) on the sentence_50Agree [financial-phrasebank + Kaggle Dataset](https://huggingface.co/datasets/nickmuchi/financial-classification), 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](https://www.kaggle.com/percyzheng/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