bert-base-cased-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0650
- Precision: 0.9325
- Recall: 0.9375
- F1: 0.9350
- Accuracy: 0.9840
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: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.2346 | 1.0 | 878 | 0.0722 | 0.9168 | 0.9217 | 0.9192 | 0.9795 |
0.0483 | 2.0 | 1756 | 0.0618 | 0.9299 | 0.9370 | 0.9335 | 0.9837 |
0.0262 | 3.0 | 2634 | 0.0650 | 0.9325 | 0.9375 | 0.9350 | 0.9840 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
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Dataset used to train SreyanG-NVIDIA/bert-base-cased-finetuned-ner
Evaluation results
- Precision on conll2003self-reported0.933
- Recall on conll2003self-reported0.937
- F1 on conll2003self-reported0.935
- Accuracy on conll2003self-reported0.984