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flan-t5-base-samsum
rishabluthra/flan-t5-base-samsum
flan-t5-base-samsum is a machine learning model from rishabluthra. 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 2, 2023
- Updated
- Oct 2, 2023
- SHA
- 22c74da8
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: 1.3724
- Rouge1: 47.1323
- Rouge2: 23.6343
- Rougel: 39.907
- Rougelsum: 43.4826
- Gen Len: 17.3260
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: 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 |
|---|---|---|---|---|---|---|---|---|
| 1.4541 | 1.0 | 1842 | 1.3788 | 46.6486 | 23.2493 | 39.359 | 43.0174 | 17.4469 |
| 1.3467 | 2.0 | 3684 | 1.3682 | 47.1945 | 23.7803 | 40.0316 | 43.5134 | 17.2295 |
| 1.2945 | 3.0 | 5526 | 1.3724 | 47.1323 | 23.6343 | 39.907 | 43.4826 | 17.3260 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Oct 2, 2023
- Updated
- Oct 2, 2023