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tathyanka-nlq-final
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0042
- Rouge2 Precision: 0.8529
- Rouge2 Recall: 0.404
- Rouge2 Fmeasure: 0.5477
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: 16
- eval_batch_size: 16
- 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 | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
---|---|---|---|---|---|---|
No log | 1.0 | 315 | 0.0138 | 0.8516 | 0.4034 | 0.5469 |
0.474 | 2.0 | 630 | 0.0081 | 0.8516 | 0.4034 | 0.5469 |
0.474 | 3.0 | 945 | 0.0054 | 0.853 | 0.4041 | 0.5478 |
0.0163 | 4.0 | 1260 | 0.0046 | 0.8529 | 0.404 | 0.5477 |
0.0093 | 5.0 | 1575 | 0.0042 | 0.8529 | 0.404 | 0.5477 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2