readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: Bert_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. -->
  
  # Bert_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: 0.9191
  - Precision: 0.7612
  - Recall: 0.8007
  - F1: 0.5106
  - Accuracy: 0.7357
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 1.0663        | 1.0   | 934  | 0.8636          | 0.6973    | 0.8467 | 0.4082 | 0.7023   |
  | 0.8354        | 2.0   | 1868 | 0.8261          | 0.7367    | 0.8086 | 0.4733 | 0.7221   |
  | 0.7164        | 3.0   | 2802 | 0.7737          | 0.7572    | 0.7988 | 0.5055 | 0.7347   |
  | 0.6149        | 4.0   | 3736 | 0.7542          | 0.7488    | 0.8402 | 0.5176 | 0.7438   |
  | 0.5153        | 5.0   | 4670 | 0.8185          | 0.7614    | 0.8123 | 0.5017 | 0.7389   |
  | 0.4314        | 6.0   | 5604 | 0.8599          | 0.7543    | 0.8259 | 0.5085 | 0.7395   |
  | 0.3689        | 7.0   | 6538 | 0.9191          | 0.7612    | 0.8007 | 0.5106 | 0.7357   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  