readme: ---
  tags:
  - generated_from_trainer
  - finance
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: finbert-tone-finetuned-finance-text-classification
    results: []
  datasets:
  - nickmuchi/financial-text-combo-classification
  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. -->
  
  # finbert-tone-finetuned-finance-text-classification
  
  This model is a fine-tuned version of [yiyanghkust/finbert-tone](https://huggingface.co/yiyanghkust/finbert-tone) on the [nickmuchi/financial-text-combo-classification](https://huggingface.co/datasets/nickmuchi/financial-text-combo-classification) dataset which is a combined dataset of financial_phrasebank,FinanceInc/auditor_sentiment and zeroshot/twitter-financial-news-sentiment.
  It achieves the following results on the evaluation set:
  - Loss: 0.6645
  - Accuracy: 0.9097
  - F1: 0.9102
  - Precision: 0.9110
  - Recall: 0.9097
  
  ## 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: 128
  - eval_batch_size: 128
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | No log        | 1.0   | 141  | 0.3934          | 0.8431   | 0.8427 | 0.8456    | 0.8431 |
  | No log        | 2.0   | 282  | 0.3214          | 0.8843   | 0.8843 | 0.8867    | 0.8843 |
  | No log        | 3.0   | 423  | 0.3302          | 0.8882   | 0.8902 | 0.8965    | 0.8882 |
  | 0.4444        | 4.0   | 564  | 0.3611          | 0.8980   | 0.8993 | 0.9026    | 0.8980 |
  | 0.4444        | 5.0   | 705  | 0.4006          | 0.8975   | 0.8987 | 0.9014    | 0.8975 |
  | 0.4444        | 6.0   | 846  | 0.4517          | 0.9037   | 0.9043 | 0.9057    | 0.9037 |
  | 0.4444        | 7.0   | 987  | 0.5324          | 0.9027   | 0.9035 | 0.9057    | 0.9027 |
  | 0.0406        | 8.0   | 1128 | 0.5308          | 0.9063   | 0.9074 | 0.9098    | 0.9063 |
  | 0.0406        | 9.0   | 1269 | 0.5586          | 0.9081   | 0.9084 | 0.9089    | 0.9081 |
  | 0.0406        | 10.0  | 1410 | 0.5783          | 0.9076   | 0.9080 | 0.9086    | 0.9076 |
  | 0.0121        | 11.0  | 1551 | 0.5741          | 0.9115   | 0.9116 | 0.9121    | 0.9115 |
  | 0.0121        | 12.0  | 1692 | 0.6288          | 0.9104   | 0.9108 | 0.9115    | 0.9104 |
  | 0.0121        | 13.0  | 1833 | 0.6328          | 0.9050   | 0.9059 | 0.9078    | 0.9050 |
  | 0.0121        | 14.0  | 1974 | 0.6887          | 0.9042   | 0.9054 | 0.9088    | 0.9042 |
  | 0.0063        | 15.0  | 2115 | 0.6345          | 0.9086   | 0.9094 | 0.9109    | 0.9086 |
  | 0.0063        | 16.0  | 2256 | 0.6545          | 0.9102   | 0.9103 | 0.9108    | 0.9102 |
  | 0.0063        | 17.0  | 2397 | 0.6585          | 0.9086   | 0.9092 | 0.9103    | 0.9086 |
  | 0.0033        | 18.0  | 2538 | 0.6676          | 0.9081   | 0.9087 | 0.9098    | 0.9081 |
  | 0.0033        | 19.0  | 2679 | 0.6614          | 0.9110   | 0.9113 | 0.9119    | 0.9110 |
  | 0.0033        | 20.0  | 2820 | 0.6645          | 0.9097   | 0.9102 | 0.9110    | 0.9097 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2