---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: flan-t5-base-xlsum
  results: []
---

<!-- 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-xlsum

This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3988

## 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: 6
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 17.0416       | 0.09  | 200   | 0.4680          |
| 0.495         | 0.18  | 400   | 0.4080          |
| 0.4721        | 0.28  | 600   | 0.4051          |
| 0.4677        | 0.37  | 800   | 0.4048          |
| 0.4691        | 0.46  | 1000  | 0.4035          |
| 0.4667        | 0.55  | 1200  | 0.4025          |
| 0.464         | 0.65  | 1400  | 0.4015          |
| 0.4575        | 0.74  | 1600  | 0.4004          |
| 0.4598        | 0.83  | 1800  | 0.4003          |
| 0.4602        | 0.92  | 2000  | 0.4004          |
| 0.4556        | 1.02  | 2200  | 0.3992          |
| 0.4335        | 1.11  | 2400  | 0.3992          |
| 0.4347        | 1.2   | 2600  | 0.3992          |
| 0.4421        | 1.29  | 2800  | 0.3999          |
| 0.4318        | 1.39  | 3000  | 0.3988          |
| 0.4425        | 1.48  | 3200  | 0.3981          |
| 0.4428        | 1.57  | 3400  | 0.3988          |
| 0.4345        | 1.66  | 3600  | 0.3980          |
| 0.4266        | 1.76  | 3800  | 0.3979          |
| 0.4245        | 1.85  | 4000  | 0.3982          |
| 0.4215        | 1.94  | 4200  | 0.3967          |
| 0.4285        | 2.03  | 4400  | 0.3977          |
| 0.4082        | 2.13  | 4600  | 0.3981          |
| 0.4049        | 2.22  | 4800  | 0.3980          |
| 0.4139        | 2.31  | 5000  | 0.3975          |
| 0.4008        | 2.4   | 5200  | 0.3983          |
| 0.4073        | 2.5   | 5400  | 0.3980          |
| 0.4214        | 2.59  | 5600  | 0.3979          |
| 0.411         | 2.68  | 5800  | 0.3983          |
| 0.4173        | 2.77  | 6000  | 0.3971          |
| 0.4098        | 2.87  | 6200  | 0.3970          |
| 0.4267        | 2.96  | 6400  | 0.3968          |
| 0.3958        | 3.05  | 6600  | 0.3976          |
| 0.3959        | 3.14  | 6800  | 0.3984          |
| 0.4049        | 3.24  | 7000  | 0.3978          |
| 0.3993        | 3.33  | 7200  | 0.3980          |
| 0.3971        | 3.42  | 7400  | 0.3983          |
| 0.4032        | 3.51  | 7600  | 0.3987          |
| 0.391         | 3.61  | 7800  | 0.3987          |
| 0.3988        | 3.7   | 8000  | 0.3990          |
| 0.3912        | 3.79  | 8200  | 0.3984          |
| 0.3958        | 3.88  | 8400  | 0.3982          |
| 0.396         | 3.98  | 8600  | 0.3979          |
| 0.3926        | 4.07  | 8800  | 0.3988          |
| 0.3913        | 4.16  | 9000  | 0.3988          |
| 0.3915        | 4.25  | 9200  | 0.3985          |
| 0.3885        | 4.35  | 9400  | 0.3988          |
| 0.3824        | 4.44  | 9600  | 0.3991          |
| 0.3884        | 4.53  | 9800  | 0.3989          |
| 0.3816        | 4.62  | 10000 | 0.3989          |
| 0.4028        | 4.72  | 10200 | 0.3986          |
| 0.3881        | 4.81  | 10400 | 0.3988          |
| 0.3809        | 4.9   | 10600 | 0.3988          |
| 0.3873        | 4.99  | 10800 | 0.3988          |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3