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T5-XSum-base
alexrodpas/T5-XSum-base
T5-XSum-base is a machine learning model from alexrodpas. 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
- Sep 23, 2023
- Updated
- Nov 7, 2023
- SHA
- 211b63b8
Base models
Datasets
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-XSum-base
This model is a fine-tuned version of t5-small on the xsum dataset. It achieves the following results on the evaluation set:
- Loss: 2.5491
- Rouge1: 0.273
- Rouge2: 0.0711
- Rougel: 0.2134
- Rougelsum: 0.2134
- Gen Len: 18.8194
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: 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: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.8234 | 1.0 | 2041 | 2.5916 | 0.2623 | 0.0647 | 0.2043 | 0.2044 | 18.8152 |
| 2.7742 | 2.0 | 4082 | 2.5577 | 0.2707 | 0.0702 | 0.2118 | 0.2117 | 18.8212 |
| 2.7482 | 3.0 | 6123 | 2.5491 | 0.273 | 0.0711 | 0.2134 | 0.2134 | 18.8194 |
Framework versions
- Transformers 4.35.0
- Pytorch 1.12.0+cu116
- Datasets 2.14.6
- Tokenizers 0.14.1
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Sep 23, 2023
- Updated
- Nov 7, 2023