metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model_index:
  - name: bert-large-uncased-whole-word-masking-ner-conll2003
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metric:
          name: Accuracy
          type: accuracy
          value: 0.9886888970085945

bert-large-uncased-whole-word-masking-ner-conll2003

This model is a fine-tuned version of bert-large-uncased-whole-word-masking on the conll2003 dataset. It achieves the following results on the evaluation set:

Loss: 0.0592
Precision: 0.9527
Recall: 0.9569
F1: 0.9548
Accuracy: 0.9887

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: 4
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.4071	1.0	877	0.0584	0.9306	0.9418	0.9362	0.9851
0.0482	2.0	1754	0.0594	0.9362	0.9491	0.9426	0.9863
0.0217	3.0	2631	0.0550	0.9479	0.9584	0.9531	0.9885
0.0103	4.0	3508	0.0592	0.9527	0.9569	0.9548	0.9887

Framework versions

Transformers 4.8.2
Pytorch 1.8.1+cu111
Datasets 1.8.0
Tokenizers 0.10.3