bert-base-finetuned-ynat
This model is a fine-tuned version of klue/bert-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6806
- F1: 0.2273
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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 2 | 1.8609 | 0.0476 |
| No log | 2.0 | 4 | 1.7637 | 0.0476 |
| No log | 3.0 | 6 | 1.6806 | 0.2273 |
| No log | 4.0 | 8 | 1.6409 | 0.2273 |
| No log | 5.0 | 10 | 1.6236 | 0.2273 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
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Model tree for min9805/bert-base-finetuned-ynat
Base model
klue/bert-base