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t5-base-finetuned-qg-context-dataset-2
Deigant/t5-base-finetuned-qg-context-dataset-2
t5-base-finetuned-qg-context-dataset-2 is a machine learning model from Deigant. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Nov 24, 2022
- Updated
- Nov 26, 2022
- SHA
- 7caee7c0
Model card
From the Hugging Face model README
<!-- 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. -->
t5-base-finetuned-qg-context-dataset-2
This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5958
- Rouge1: 37.1698
- Rouge2: 15.8177
- Rougel: 33.3329
- Rougelsum: 33.0872
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 73 | 1.8700 | 30.0504 | 9.8379 | 24.0514 | 24.1584 |
| No log | 2.0 | 146 | 1.6264 | 34.715 | 12.71 | 27.825 | 27.7697 |
| No log | 3.0 | 219 | 1.5904 | 33.9221 | 10.688 | 29.5483 | 29.5388 |
| No log | 4.0 | 292 | 1.5623 | 36.5544 | 14.8003 | 31.6295 | 31.4603 |
| No log | 5.0 | 365 | 1.5463 | 34.071 | 13.189 | 30.1517 | 30.3325 |
| No log | 6.0 | 438 | 1.5539 | 37.7324 | 15.5312 | 33.3968 | 33.2518 |
| 1.5099 | 7.0 | 511 | 1.5643 | 32.5168 | 11.2479 | 27.4951 | 27.4425 |
| 1.5099 | 8.0 | 584 | 1.5653 | 39.5646 | 17.9528 | 35.4095 | 35.2042 |
| 1.5099 | 9.0 | 657 | 1.5679 | 39.333 | 17.0059 | 34.9131 | 34.7696 |
| 1.5099 | 10.0 | 730 | 1.5757 | 37.5046 | 16.2468 | 32.5031 | 32.4012 |
| 1.5099 | 11.0 | 803 | 1.5738 | 37.601 | 16.4592 | 33.5804 | 33.1352 |
| 1.5099 | 12.0 | 876 | 1.5894 | 42.1889 | 19.3169 | 37.8273 | 37.7312 |
| 1.5099 | 13.0 | 949 | 1.5929 | 38.5814 | 17.0896 | 34.4696 | 34.3629 |
| 1.015 | 14.0 | 1022 | 1.5922 | 36.6392 | 16.8083 | 32.6318 | 32.4199 |
| 1.015 | 15.0 | 1095 | 1.5948 | 34.6707 | 15.7198 | 30.319 | 30.3403 |
| 1.015 | 16.0 | 1168 | 1.5958 | 37.1698 | 15.8177 | 33.3329 | 33.0872 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Nov 24, 2022
- Updated
- Nov 26, 2022