distilbert-base-cased-finetuned-ner
This model is a fine-tuned version of distilbert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0859
- Precision: 0.9042
- Recall: 0.9036
- F1: 0.9039
- Accuracy: 0.9761
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: 64
- eval_batch_size: 64
- 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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 220 | 0.1299 | 0.8569 | 0.8390 | 0.8478 | 0.9624 |
| No log | 2.0 | 440 | 0.0930 | 0.8954 | 0.8944 | 0.8949 | 0.9740 |
| 0.2024 | 3.0 | 660 | 0.0859 | 0.9042 | 0.9036 | 0.9039 | 0.9761 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for ssiyer/distilbert-base-cased-finetuned-ner
Base model
distilbert/distilbert-base-casedDataset used to train ssiyer/distilbert-base-cased-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.904
- Recall on conll2003validation set self-reported0.904
- F1 on conll2003validation set self-reported0.904
- Accuracy on conll2003validation set self-reported0.976