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albert-base-v2-finetuned-ner
This model is a fine-tuned version of albert-base-v2 on the plod-filtered dataset. It achieves the following results on the evaluation set:
- Loss: 0.0319
- Precision: 0.9890
- Recall: 0.9881
- F1: 0.9886
- Accuracy: 0.9884
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0649 | 1.0 | 3018 | 0.0471 | 0.9838 | 0.9814 | 0.9826 | 0.9818 |
| 0.0442 | 2.0 | 6036 | 0.0319 | 0.9890 | 0.9881 | 0.9886 | 0.9884 |
Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
