scibert-finetuned-ner-dmdd
This model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0121
- Precision: 0.9717
- Recall: 0.9820
- F1: 0.9768
- Accuracy: 0.9968
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: 64
- eval_batch_size: 64
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.03 | 1.0 | 3803 | 0.0152 | 0.9536 | 0.9754 | 0.9644 | 0.9954 |
| 0.0182 | 2.0 | 7606 | 0.0115 | 0.9664 | 0.9805 | 0.9734 | 0.9965 |
| 0.0018 | 3.0 | 11409 | 0.0121 | 0.9717 | 0.9820 | 0.9768 | 0.9968 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
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Base model
allenai/scibert_scivocab_cased