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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: 47.6993
---
<!-- 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: 1.3538
- Rouge1: 47.6993
- Rouge2: 24.0887
- Rougel: 40.2819
- Rougelsum: 43.8375
- Gen Len: 17.0842
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.4327 | 1.0 | 1841 | 1.3620 | 47.1926 | 23.4593 | 39.7385 | 43.2514 | 17.0623 |
| 1.3235 | 2.0 | 3683 | 1.3563 | 46.7874 | 23.1964 | 39.4248 | 42.9616 | 16.9585 |
| 1.2477 | 3.0 | 5524 | 1.3538 | 47.6993 | 24.0887 | 40.2819 | 43.8375 | 17.0842 |
| 1.208 | 4.0 | 7366 | 1.3555 | 47.6355 | 24.0054 | 40.1665 | 43.6581 | 17.0965 |
| 1.193 | 5.0 | 9205 | 1.3563 | 47.6582 | 23.9906 | 40.1561 | 43.7082 | 17.1477 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0
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