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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan-T5-base-sum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: test
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 47.6617
---
<!-- 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-sum
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co./google/flan-t5-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3721
- Rouge1: 47.6617
- Rouge2: 23.7647
- Rougel: 40.1155
- Rougelsum: 43.6943
- Gen Len: 17.2759
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.4403 | 1.0 | 1842 | 1.3822 | 47.2814 | 23.7835 | 39.7427 | 43.4897 | 17.0256 |
| 1.3572 | 2.0 | 3684 | 1.3747 | 47.553 | 23.5714 | 39.8212 | 43.6246 | 17.4420 |
| 1.2822 | 3.0 | 5526 | 1.3721 | 47.6617 | 23.7647 | 40.1155 | 43.6943 | 17.2759 |
| 1.2375 | 4.0 | 7368 | 1.3764 | 47.7453 | 24.1099 | 40.1684 | 43.8659 | 17.2943 |
| 1.1935 | 5.0 | 9210 | 1.3780 | 47.614 | 23.6643 | 39.8434 | 43.6558 | 17.3077 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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