metadata

license: apache-2.0
tags:
  - generated_from_trainer
base_model: bert-base-uncased
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: NER_Pittsburgh_TAA
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: validation
          args: conll2003
        metrics:
          - type: precision
            value: 0.9429236395877203
            name: Precision
          - type: recall
            value: 0.9517843159190066
            name: Recall
          - type: f1
            value: 0.9473332591025497
            name: F1
          - type: accuracy
            value: 0.9867030994328562
            name: Accuracy

NER_Pittsburgh_TAA

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

Loss: 0.0860
Precision: 0.9429
Recall: 0.9518
F1: 0.9473
Accuracy: 0.9867

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: 32
eval_batch_size: 32
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
No log	1.0	439	0.0863	0.9437	0.9444	0.9440	0.9861
0.0024	2.0	878	0.0995	0.9394	0.9442	0.9418	0.9852
0.0021	3.0	1317	0.0904	0.9355	0.9463	0.9409	0.9856
0.0012	4.0	1756	0.0835	0.9427	0.9514	0.9471	0.9867
0.0009	5.0	2195	0.0860	0.9429	0.9518	0.9473	0.9867

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1