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flan-t5-base-eLife
tanishq1420/flan-t5-base-eLife
flan-t5-base-eLife is a machine learning model from tanishq1420. 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
- Apr 24, 2024
- Updated
- Apr 24, 2024
- SHA
- 313bad54
Base models
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. -->
flan-t5-base-eLife
This model is a fine-tuned version of google/flan-t5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.0883
- Rouge1: 0.3665
- Rouge2: 0.0826
- Rougel: 0.3524
- Bertscore: 0.8322
- Readability: 8.6631
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: 0.0001
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bertscore | Readability |
|---|---|---|---|---|---|---|---|---|
| 2.1756 | 1.0 | 544 | 2.1228 | 0.3665 | 0.0834 | 0.3529 | 0.8339 | 8.6929 |
| 2.1907 | 2.0 | 1088 | 2.0947 | 0.3545 | 0.0804 | 0.3418 | 0.8290 | 8.5141 |
| 2.1566 | 3.0 | 1632 | 2.0883 | 0.3665 | 0.0826 | 0.3524 | 0.8322 | 8.6631 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.2.1
- Datasets 2.19.0
- Tokenizers 0.15.2
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Apr 24, 2024
- Updated
- Apr 24, 2024