readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: Longformer_v5
    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_v5
  
  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: 0.7919
  - Precision: 0.8516
  - Recall: 0.8678
  - F1: 0.6520
  - Accuracy: 0.8259
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.7744        | 1.0   | 1012 | 0.5785          | 0.8375    | 0.8501 | 0.5798 | 0.8098   |
  | 0.5211        | 2.0   | 2024 | 0.5415          | 0.8434    | 0.8801 | 0.6251 | 0.8282   |
  | 0.3996        | 3.0   | 3036 | 0.5565          | 0.8500    | 0.8766 | 0.6303 | 0.8274   |
  | 0.2964        | 4.0   | 4048 | 0.6017          | 0.8617    | 0.8546 | 0.6415 | 0.8240   |
  | 0.2187        | 5.0   | 5060 | 0.6660          | 0.8485    | 0.8718 | 0.6431 | 0.8271   |
  | 0.1603        | 6.0   | 6072 | 0.7235          | 0.8493    | 0.8759 | 0.6544 | 0.8290   |
  | 0.1208        | 7.0   | 7084 | 0.7919          | 0.8516    | 0.8678 | 0.6520 | 0.8259   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  