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GSOCt5-small-finetuned-t5_V1
Seungjun/GSOCt5-small-finetuned-t5_V1
GSOCt5-small-finetuned-t5_V1 is a machine learning model from Seungjun. 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
- Jun 26, 2023
- Updated
- Jun 26, 2023
- SHA
- 6601c9f2
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. -->
GSOCt5-small-finetuned-t5_V1
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6901
- Rouge1: 33.0453
- Rouge2: 23.5726
- Rougel: 30.4702
- Rougelsum: 31.6567
- Gen Len: 18.9571
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: 3e-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
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 0.8364 | 1.0 | 631 | 0.6993 | 32.9852 | 23.5209 | 30.3671 | 31.5511 | 18.9518 |
| 0.8027 | 2.0 | 1262 | 0.6901 | 33.0453 | 23.5726 | 30.4702 | 31.6567 | 18.9571 |
| 0.7988 | 3.0 | 1893 | 0.6901 | 33.0453 | 23.5726 | 30.4702 | 31.6567 | 18.9571 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Jun 26, 2023
- Updated
- Jun 26, 2023