readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - tweet_eval
  metrics:
  - f1
  model-index:
  - name: hate_trained
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: tweet_eval
        type: tweet_eval
        args: hate
      metrics:
      - name: F1
        type: f1
        value: 0.7730369969869401
  ---
  
  <!-- 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. -->
  
  # hate_trained
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9661
  - F1: 0.7730
  
  ## 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: 9.303025140957233e-06
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 0
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.4767        | 1.0   | 2250 | 0.5334          | 0.7717 |
  | 0.4342        | 2.0   | 4500 | 0.7633          | 0.7627 |
  | 0.3813        | 3.0   | 6750 | 0.9452          | 0.7614 |
  | 0.3118        | 4.0   | 9000 | 0.9661          | 0.7730 |
  
  
  ### Framework versions
  
  - Transformers 4.13.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  