<!-- 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-base-uncased-finetuned-cnn
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.2647
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.2811 | 1.0 | 157 | 2.3283 |
2.3086 | 2.0 | 314 | 2.3172 |
2.3472 | 3.0 | 471 | 2.3033 |
2.3608 | 4.0 | 628 | 2.2989 |
2.3494 | 5.0 | 785 | 2.2975 |
2.3217 | 6.0 | 942 | 2.2701 |
2.3087 | 7.0 | 1099 | 2.2545 |
2.291 | 8.0 | 1256 | 2.2376 |
2.2983 | 9.0 | 1413 | 2.2653 |
2.2892 | 10.0 | 1570 | 2.2647 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1