readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-ner
    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-finetuned-ner
  
  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: 0.6434
  - Precision: 0.8589
  - Recall: 0.8686
  - F1: 0.8637
  - Accuracy: 0.8324
  
  ## 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.615         | 1.0   | 1741 | 0.6111          | 0.8200    | 0.8652 | 0.8420 | 0.8046   |
  | 0.4795        | 2.0   | 3482 | 0.5366          | 0.8456    | 0.8803 | 0.8626 | 0.8301   |
  | 0.3705        | 3.0   | 5223 | 0.5412          | 0.8527    | 0.8786 | 0.8655 | 0.8339   |
  | 0.2749        | 4.0   | 6964 | 0.5906          | 0.8559    | 0.8711 | 0.8634 | 0.8316   |
  | 0.2049        | 5.0   | 8705 | 0.6434          | 0.8589    | 0.8686 | 0.8637 | 0.8324   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.4
  - Tokenizers 0.11.6
  