---
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: 42.6857
---

<!-- 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: 42.6857
- Rouge2: 18.3487
- Rougel: 35.3275
- Rougelsum: 38.8882
- 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          | 42.4897 | 18.3435 | 35.1269 | 38.7613   | 16.6532 |
| 1.8578        | 0.7   | 200  | 1.6878          | 42.0049 | 18.2725 | 34.9941 | 38.4372   | 16.7216 |
| 1.835         | 1.06  | 300  | 1.6823          | 42.7659 | 18.64   | 35.4591 | 38.9559   | 16.9048 |
| 1.8144        | 1.41  | 400  | 1.6786          | 42.61   | 18.3931 | 35.3777 | 38.8516   | 16.6618 |
| 1.8094        | 1.76  | 500  | 1.6754          | 42.6857 | 18.3487 | 35.3275 | 38.8882   | 16.8474 |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0