readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: destilbert_uncased_fever_nli
    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. -->
  
  # destilbert_uncased_fever_nli
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a subset of [fever_nli](https://huggingface.co/datasets/pietrolesci/nli_fever) dataset by using the first 7.5k datapoints per each label from the training split.
  It achieves the following results on the evaluation set:
  - Loss: 2.1829
  - F1: 0.7045
  
  ## 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: 0.0001
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | No log        | 1.0   | 352  | 0.7894          | 0.7029 |
  | 0.5462        | 2.0   | 704  | 0.9908          | 0.7097 |
  | 0.2922        | 3.0   | 1056 | 1.0831          | 0.6924 |
  | 0.2922        | 4.0   | 1408 | 1.2833          | 0.7044 |
  | 0.142         | 5.0   | 1760 | 1.4096          | 0.7008 |
  | 0.0695        | 6.0   | 2112 | 1.5585          | 0.7013 |
  | 0.0695        | 7.0   | 2464 | 1.7262          | 0.7015 |
  | 0.0434        | 8.0   | 2816 | 2.0138          | 0.7016 |
  | 0.0204        | 9.0   | 3168 | 2.0912          | 0.7012 |
  | 0.011         | 10.0  | 3520 | 2.1829          | 0.7045 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  