Distilbert-base-uncased-xsum-factuality
This model is a fine-tuned version of distilbert-base-uncased on the XSum-Factuality dataset.
You can view more implementation details as part of this GitHub repository. It achieves the following results on the evaluation set:
- Loss: 0.6850
- Accuracy: 0.6332
- F1: 0.6212
- Precision: 0.6526
- Recall: 0.6332
Weights and Biases Documentation
View the full run on Weights & Biases
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: 1e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
Training results
| Training Loss |
Epoch |
Step |
Validation Loss |
Accuracy |
F1 |
Precision |
Recall |
| 0.6904 |
6.93 |
1040 |
0.6850 |
0.6332 |
0.6212 |
0.6526 |
0.6332 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
