flan-t5-base-samsum / README.md
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---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan-t5-base-samsum
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: 47.2702
---
<!-- 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](https://huggingface.co/google/flan-t5-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3743
- Rouge1: 47.2702
- Rouge2: 23.5181
- Rougel: 39.8232
- Rougelsum: 43.3645
- Gen Len: 17.2466
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.4528 | 1.0 | 1842 | 1.3867 | 46.7028 | 22.7798 | 39.0236 | 42.7977 | 17.3993 |
| 1.3385 | 2.0 | 3684 | 1.3751 | 46.9306 | 23.2467 | 39.1768 | 42.9745 | 17.3431 |
| 1.2711 | 3.0 | 5526 | 1.3745 | 47.2857 | 23.5195 | 39.6304 | 43.3844 | 17.4493 |
| 1.2284 | 4.0 | 7368 | 1.3743 | 47.2702 | 23.5181 | 39.8232 | 43.3645 | 17.2466 |
| 1.2085 | 5.0 | 9210 | 1.3759 | 47.4235 | 23.7621 | 39.8577 | 43.5762 | 17.2967 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0