test-ner
This model is a fine-tuned version of roberta-base on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0398
- Precision: 0.9468
- Recall: 0.9579
- F1: 0.9523
- Accuracy: 0.9921
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: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: IPU
- total_train_batch_size: 16
- total_eval_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- training precision: Mixed Precision
Training results
Framework versions
- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1
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Dataset used to train jimypbr/test-ner
Evaluation results
- Precision on conll2003self-reported0.947
- Recall on conll2003self-reported0.958
- F1 on conll2003self-reported0.952
- Accuracy on conll2003self-reported0.992