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---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan-t5-small-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: 42.6766
---
<!-- 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-small-samsum
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6754
- Rouge1: 42.6766
- Rouge2: 18.3823
- Rougel: 35.2377
- Rougelsum: 38.9432
- Gen Len: 16.8474
## 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: 52
- eval_batch_size: 52
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.8824 | 0.35 | 100 | 1.7015 | 42.4882 | 18.3559 | 35.0843 | 38.8515 | 16.6532 |
| 1.8578 | 0.7 | 200 | 1.6878 | 42.0007 | 18.2614 | 34.9474 | 38.4992 | 16.7216 |
| 1.835 | 1.06 | 300 | 1.6823 | 42.7535 | 18.6418 | 35.3807 | 39.0098 | 16.9048 |
| 1.8144 | 1.41 | 400 | 1.6786 | 42.6326 | 18.4197 | 35.2995 | 38.9165 | 16.6618 |
| 1.8094 | 1.76 | 500 | 1.6754 | 42.6766 | 18.3823 | 35.2377 | 38.9432 | 16.8474 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
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