readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: Longformer-finetuned-norm
    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-norm
  
  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: 0.8127
  - Precision: 0.8429
  - Recall: 0.8701
  - F1: 0.8562
  - Accuracy: 0.8221
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.8008        | 1.0   | 1012 | 0.5839          | 0.8266    | 0.8637 | 0.8447 | 0.8084   |
  | 0.5168        | 2.0   | 2024 | 0.5927          | 0.7940    | 0.9102 | 0.8481 | 0.8117   |
  | 0.3936        | 3.0   | 3036 | 0.5651          | 0.8476    | 0.8501 | 0.8488 | 0.8143   |
  | 0.2939        | 4.0   | 4048 | 0.6411          | 0.8494    | 0.8578 | 0.8536 | 0.8204   |
  | 0.2165        | 5.0   | 5060 | 0.6833          | 0.8409    | 0.8822 | 0.8611 | 0.8270   |
  | 0.1561        | 6.0   | 6072 | 0.7643          | 0.8404    | 0.8810 | 0.8602 | 0.8259   |
  | 0.1164        | 7.0   | 7084 | 0.8127          | 0.8429    | 0.8701 | 0.8562 | 0.8221   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  