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eddiegulay
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g-bert-NER

Token Classification
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bert
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How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("token-classification", model="eddiegulay/g-bert-NER")
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("eddiegulay/g-bert-NER")
model = AutoModelForTokenClassification.from_pretrained("eddiegulay/g-bert-NER")
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g-bert-NER

This model is a fine-tuned version of google-bert/bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1387
  • Precision: 0.8925
  • Recall: 0.9102
  • F1: 0.9013
  • Accuracy: 0.9800

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.1773 1.0 878 0.1028 0.8910 0.8947 0.8928 0.9781
0.036 2.0 1756 0.1125 0.8901 0.9132 0.9015 0.9793
0.0194 3.0 2634 0.1202 0.8948 0.9093 0.9020 0.9800
0.0112 4.0 3512 0.1346 0.8889 0.9136 0.9011 0.9794
0.0081 5.0 4390 0.1387 0.8925 0.9102 0.9013 0.9800

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1
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google-bert/bert-base-uncased
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Dataset used to train eddiegulay/g-bert-NER

Evaluation results