albert-base-v2_mnli_bc
This model is a fine-tuned version of albert-base-v2 on the GLUE MNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.2952
- Accuracy: 0.9399
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2159 | 1.0 | 16363 | 0.2268 | 0.9248 |
| 0.1817 | 2.0 | 32726 | 0.2335 | 0.9347 |
| 0.0863 | 3.0 | 49089 | 0.3014 | 0.9401 |
Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
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