<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
distilbert_add_GLUE_Experiment_mnli
This model is a fine-tuned version of distilbert-base-uncased on the GLUE MNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.9474
- Accuracy: 0.5358
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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
1.0947 | 1.0 | 1534 | 1.0842 | 0.3763 |
1.0512 | 2.0 | 3068 | 1.0320 | 0.4501 |
0.9934 | 3.0 | 4602 | 0.9839 | 0.4935 |
0.9689 | 4.0 | 6136 | 0.9703 | 0.4942 |
0.953 | 5.0 | 7670 | 0.9731 | 0.5038 |
0.9377 | 6.0 | 9204 | 0.9563 | 0.5152 |
0.9191 | 7.0 | 10738 | 0.9544 | 0.5311 |
0.9014 | 8.0 | 12272 | 0.9629 | 0.5164 |
0.883 | 9.0 | 13806 | 0.9817 | 0.5301 |
0.865 | 10.0 | 15340 | 0.9691 | 0.5209 |
0.8452 | 11.0 | 16874 | 0.9606 | 0.5456 |
0.8227 | 12.0 | 18408 | 0.9846 | 0.5341 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2