metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wnut_17
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-large-uncased_ner_wnut_17
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wnut_17
          type: wnut_17
          args: wnut_17
        metrics:
          - name: Precision
            type: precision
            value: 0.7052785923753666
          - name: Recall
            type: recall
            value: 0.5753588516746412
          - name: F1
            type: f1
            value: 0.6337285902503295
          - name: Accuracy
            type: accuracy
            value: 0.9602644796236252

bert-large-uncased_ner_wnut_17

This model is a fine-tuned version of bert-large-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:

Loss: 0.2516
Precision: 0.7053
Recall: 0.5754
F1: 0.6337
Accuracy: 0.9603

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: cosine
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	213	0.2143	0.6353	0.4605	0.5340	0.9490
No log	2.0	426	0.2299	0.7322	0.5036	0.5967	0.9556
0.1489	3.0	639	0.2137	0.6583	0.5945	0.6248	0.9603
0.1489	4.0	852	0.2494	0.7035	0.5789	0.6352	0.9604
0.0268	5.0	1065	0.2516	0.7053	0.5754	0.6337	0.9603

Framework versions

Transformers 4.20.1
Pytorch 1.11.0
Datasets 2.1.0
Tokenizers 0.12.1