distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the conll2002 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2240
- Precision: 0.6719
- Recall: 0.6660
- F1: 0.6689
- Accuracy: 0.9377
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.3562 | 1.0 | 521 | 0.2669 | 0.6139 | 0.5870 | 0.6001 | 0.9250 |
0.1976 | 2.0 | 1042 | 0.2408 | 0.6180 | 0.6697 | 0.6428 | 0.9303 |
0.1519 | 3.0 | 1563 | 0.2240 | 0.6719 | 0.6660 | 0.6689 | 0.9377 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for raulgdp/distilbert-base-uncased-finetuned-ner
Base model
distilbert/distilbert-base-uncasedDataset used to train raulgdp/distilbert-base-uncased-finetuned-ner
Evaluation results
- Precision on conll2002validation set self-reported0.672
- Recall on conll2002validation set self-reported0.666
- F1 on conll2002validation set self-reported0.669
- Accuracy on conll2002validation set self-reported0.938