readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: mdeberta-hate-final
    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. -->
  
  # mdeberta-hate-final
  
  This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6223
  - Accuracy: 0.7424
  - Precision: 0.7410
  - Recall: 0.7424
  - F1: 0.7363
  
  ## 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: 1e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | No log        | 1.0   | 296  | 0.5309          | 0.7519   | 0.7685    | 0.7519 | 0.7357 |
  | 0.5358        | 2.0   | 592  | 0.5228          | 0.7510   | 0.7663    | 0.7510 | 0.7351 |
  | 0.5358        | 3.0   | 888  | 0.5565          | 0.7510   | 0.7513    | 0.7510 | 0.7438 |
  | 0.4295        | 4.0   | 1184 | 0.5639          | 0.7481   | 0.7488    | 0.7481 | 0.7403 |
  | 0.4295        | 5.0   | 1480 | 0.5941          | 0.7510   | 0.7531    | 0.7510 | 0.7423 |
  | 0.3701        | 6.0   | 1776 | 0.6223          | 0.7424   | 0.7410    | 0.7424 | 0.7363 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  