readme: ---
  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: 0.8840
  ---
  
  <!-- 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. -->
  
  # finbert-tone-finetuned-fintwitter-classification
  
  This model is a fine-tuned version of [yiyanghkust/finbert-tone](https://huggingface.co/yiyanghkust/finbert-tone) on [Twitter Financial News](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment) 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
  