readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: Longformer-finetuned-comp5
    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. -->
  
  # Longformer-finetuned-comp5
  
  This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8180
  - Precision: 0.5680
  - Recall: 0.7490
  - F1: 0.6430
  - Accuracy: 0.6430
  
  ## 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: 5e-05
  - train_batch_size: 1
  - eval_batch_size: 1
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 8
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 7
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.8296        | 1.0   | 1012 | 0.5801          | 0.4806    | 0.6633 | 0.5448 | 0.5448   |
  | 0.5367        | 2.0   | 2024 | 0.5386          | 0.5617    | 0.7042 | 0.6172 | 0.6172   |
  | 0.4109        | 3.0   | 3036 | 0.5755          | 0.5590    | 0.7261 | 0.6248 | 0.6248   |
  | 0.3088        | 4.0   | 4048 | 0.6167          | 0.5775    | 0.7394 | 0.6435 | 0.6435   |
  | 0.2234        | 5.0   | 5060 | 0.7098          | 0.5626    | 0.7477 | 0.6370 | 0.6370   |
  | 0.1637        | 6.0   | 6072 | 0.7399          | 0.5742    | 0.7413 | 0.6438 | 0.6438   |
  | 0.1236        | 7.0   | 7084 | 0.8180          | 0.5680    | 0.7490 | 0.6430 | 0.6430   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  