distilbert-base-multilingual-cased-finetuned-ner
This model is a fine-tuned version of distilbert/distilbert-base-multilingual-cased on the conllpp dataset. It achieves the following results on the evaluation set:
- Loss: 0.0632
- Precision: 0.9282
- Recall: 0.9340
- F1: 0.9311
- Accuracy: 0.9839
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 OptimizerNames.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 |
---|---|---|---|---|---|---|---|
0.237 | 1.0 | 878 | 0.0732 | 0.9083 | 0.9188 | 0.9135 | 0.9794 |
0.0533 | 2.0 | 1756 | 0.0648 | 0.9265 | 0.9274 | 0.9269 | 0.9827 |
0.0303 | 3.0 | 2634 | 0.0632 | 0.9282 | 0.9340 | 0.9311 | 0.9839 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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Dataset used to train janko/distilbert-base-multilingual-cased-finetuned-ner
Evaluation results
- Precision on conllppvalidation set self-reported0.928
- Recall on conllppvalidation set self-reported0.934
- F1 on conllppvalidation set self-reported0.931
- Accuracy on conllppvalidation set self-reported0.984