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bert-finetuned-ner

This model is a fine-tuned version of 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
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