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t5_finetuned_paraphrase
Ujjawal/t5_finetuned_paraphrase
t5_finetuned_paraphrase is a machine learning model from Ujjawal. 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
- Oct 22, 2023
- Updated
- Oct 22, 2023
- SHA
- 5473f638
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_finetuned_paraphrase
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8386
- Rouge1: 67.2398
- Rouge2: 47.8969
- Rougel: 63.7914
- Rougelsum: 63.7998
- Gen Len: 18.039
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: 8
- eval_batch_size: 8
- 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 |
|---|---|---|---|---|---|---|---|---|
| 0.9806 | 1.0 | 6250 | 0.8724 | 66.8051 | 47.0687 | 63.2656 | 63.2786 | 18.078 |
| 0.957 | 2.0 | 12500 | 0.8446 | 67.1024 | 47.6199 | 63.5856 | 63.6021 | 18.0458 |
| 0.9293 | 3.0 | 18750 | 0.8386 | 67.2398 | 47.8969 | 63.7914 | 63.7998 | 18.039 |
Framework versions
- Transformers 4.27.3
- Pytorch 1.7.1+cu101
- Datasets 2.13.2
- Tokenizers 0.13.2
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Oct 22, 2023
- Updated
- Oct 22, 2023