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token_classification_finetune
This model is a fine-tuned version of albert-base-v2 on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2489
- Precision: 0.5760
- Recall: 0.3513
- F1: 0.4364
- Accuracy: 0.9444
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: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 107 | 0.2573 | 0.6011 | 0.3003 | 0.4005 | 0.9409 |
| No log | 2.0 | 214 | 0.2489 | 0.5760 | 0.3513 | 0.4364 | 0.9444 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
