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t5-base-finetuned-p7_V2
laurenmit/t5-base-finetuned-p7_V2
t5-base-finetuned-p7_V2 is a machine learning model from laurenmit. 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
- May 24, 2023
- Updated
- May 24, 2023
- SHA
- 9cd78685
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-p7_V2
This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5882
- Rouge1: 53.6225
- Rouge2: 43.8285
- Rougel: 51.4683
- Rougelsum: 51.8848
- Gen Len: 18.2368
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: 1
- eval_batch_size: 1
- 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 |
|---|---|---|---|---|---|---|---|---|
| 0.7305 | 1.0 | 910 | 0.6238 | 52.767 | 42.9846 | 50.5281 | 50.9489 | 18.3125 |
| 0.4819 | 2.0 | 1820 | 0.5835 | 53.6526 | 44.0044 | 51.6286 | 51.9821 | 18.2533 |
| 0.4193 | 3.0 | 2730 | 0.5882 | 53.6225 | 43.8285 | 51.4683 | 51.8848 | 18.2368 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- May 24, 2023
- Updated
- May 24, 2023