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flan-t5-base-samsum
rishavsinghi/flan-t5-base-samsum
flan-t5-base-samsum is a machine learning model from rishavsinghi. 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 26, 2025
- Updated
- Apr 26, 2025
- SHA
- 9a180632
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. -->
flan-t5-base-samsum
This model is a fine-tuned version of google/flan-t5-base on the samsum dataset. It achieves the following results on the evaluation set:
- Loss: 1.4256
- Rouge1: 46.7518
- Rouge2: 22.6264
- Rougel: 38.9444
- Rougelsum: 43.1629
- Gen Len: 17.9567
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 250 | 1.4186 | 46.8348 | 22.3454 | 38.8329 | 43.2308 | 17.5567 |
| 1.4551 | 2.0 | 500 | 1.4256 | 46.7518 | 22.6264 | 38.9444 | 43.1629 | 17.9567 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Apr 26, 2025
- Updated
- Apr 26, 2025