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bert-tiny-finetuned-wnut17-ner

This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the wnut_17 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6054
  • Precision: 0.0
  • Recall: 0.0
  • F1: 0.0
  • Accuracy: 0.8961

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: 2e-05
  • train_batch_size: 128
  • eval_batch_size: 128
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 27 1.1060 0.0 0.0 0.0 0.8961
No log 2.0 54 0.9075 0.0 0.0 0.0 0.8961
No log 3.0 81 0.7978 0.0 0.0 0.0 0.8961
No log 4.0 108 0.7333 0.0 0.0 0.0 0.8961
No log 5.0 135 0.6929 0.0 0.0 0.0 0.8961
No log 6.0 162 0.6661 0.0 0.0 0.0 0.8961
No log 7.0 189 0.6477 0.0 0.0 0.0 0.8961
No log 8.0 216 0.6346 0.0 0.0 0.0 0.8961
No log 9.0 243 0.6251 0.0 0.0 0.0 0.8961
No log 10.0 270 0.6182 0.0 0.0 0.0 0.8961
No log 11.0 297 0.6132 0.0 0.0 0.0 0.8961
No log 12.0 324 0.6097 0.0 0.0 0.0 0.8961
No log 13.0 351 0.6073 0.0 0.0 0.0 0.8961
No log 14.0 378 0.6059 0.0 0.0 0.0 0.8961
No log 15.0 405 0.6054 0.0 0.0 0.0 0.8961

Framework versions

  • Transformers 4.21.0
  • Pytorch 1.12.0+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1
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Dataset used to train muhtasham/bert-tiny-finetuned-wnut17-ner

Evaluation results