readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: fb-data2vec-finetuned-finance-classification
    results: []
  ---
  
  <!-- 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. -->
  
  # fb-data2vec-finetuned-finance-classification
  
  This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/facebook/data2vec-text-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8993
  - Accuracy: 0.8557
  - F1: 0.8563
  - Precision: 0.8576
  - Recall: 0.8557
  
  ## 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: 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: 15
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | No log        | 1.0   | 285  | 0.6704          | 0.6680   | 0.6262 | 0.7919    | 0.6680 |
  | 0.6626        | 2.0   | 570  | 0.4731          | 0.8360   | 0.8350 | 0.8346    | 0.8360 |
  | 0.6626        | 3.0   | 855  | 0.4598          | 0.8458   | 0.8454 | 0.8452    | 0.8458 |
  | 0.3666        | 4.0   | 1140 | 0.4758          | 0.8360   | 0.8352 | 0.8353    | 0.8360 |
  | 0.3666        | 5.0   | 1425 | 0.5683          | 0.8340   | 0.8342 | 0.8353    | 0.8340 |
  | 0.2316        | 6.0   | 1710 | 0.6234          | 0.8419   | 0.8421 | 0.8447    | 0.8419 |
  | 0.2316        | 7.0   | 1995 | 0.7186          | 0.8379   | 0.8385 | 0.8395    | 0.8379 |
  | 0.1523        | 8.0   | 2280 | 0.7268          | 0.8439   | 0.8442 | 0.8455    | 0.8439 |
  | 0.0928        | 9.0   | 2565 | 0.7364          | 0.8439   | 0.8452 | 0.8494    | 0.8439 |
  | 0.0928        | 10.0  | 2850 | 0.7975          | 0.8478   | 0.8476 | 0.8476    | 0.8478 |
  | 0.054         | 11.0  | 3135 | 0.9019          | 0.8498   | 0.8509 | 0.8554    | 0.8498 |
  | 0.054         | 12.0  | 3420 | 0.8779          | 0.8538   | 0.8548 | 0.8578    | 0.8538 |
  | 0.036         | 13.0  | 3705 | 0.8914          | 0.8617   | 0.8626 | 0.8652    | 0.8617 |
  | 0.036         | 14.0  | 3990 | 0.8976          | 0.8538   | 0.8547 | 0.8572    | 0.8538 |
  | 0.0232        | 15.0  | 4275 | 0.8993          | 0.8557   | 0.8563 | 0.8576    | 0.8557 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  