---
library_name: transformers
license: apache-2.0
base_model: google/flan-t5-base
tags:
- simplification
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-base-lecturaFacil2
  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-lecturaFacil2

This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9248
- Rouge1: 7.2543
- Rouge2: 4.93
- Rougel: 6.8455
- Rougelsum: 7.1492

## 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: 5.6e-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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log        | 1.0   | 126  | 1.0730          | 7.2577 | 5.0999 | 6.93   | 7.1434    |
| 1.2321        | 2.0   | 252  | 1.0311          | 7.1992 | 4.8058 | 6.8141 | 7.0763    |
| 1.2321        | 3.0   | 378  | 1.0080          | 7.3086 | 5.0542 | 6.9555 | 7.1842    |
| 1.0716        | 4.0   | 504  | 0.9922          | 7.4007 | 5.103  | 7.0486 | 7.2928    |
| 1.0716        | 5.0   | 630  | 0.9780          | 7.371  | 4.8914 | 6.9432 | 7.2608    |
| 1.0098        | 6.0   | 756  | 0.9741          | 7.437  | 4.9959 | 6.9879 | 7.3239    |
| 1.0098        | 7.0   | 882  | 0.9598          | 7.4599 | 5.0313 | 7.0512 | 7.3389    |
| 0.9732        | 8.0   | 1008 | 0.9569          | 7.3745 | 4.9056 | 6.9218 | 7.2723    |
| 0.9732        | 9.0   | 1134 | 0.9509          | 7.4509 | 5.0068 | 6.977  | 7.3181    |
| 0.9362        | 10.0  | 1260 | 0.9453          | 7.315  | 4.9174 | 6.8758 | 7.2008    |
| 0.9362        | 11.0  | 1386 | 0.9387          | 7.3426 | 4.967  | 6.9522 | 7.2339    |
| 0.9081        | 12.0  | 1512 | 0.9407          | 7.4108 | 5.0262 | 6.9448 | 7.2856    |
| 0.9081        | 13.0  | 1638 | 0.9364          | 7.3558 | 4.9637 | 6.9302 | 7.2288    |
| 0.8894        | 14.0  | 1764 | 0.9339          | 7.3624 | 4.9192 | 6.923  | 7.2278    |
| 0.8894        | 15.0  | 1890 | 0.9320          | 7.3651 | 4.9105 | 6.9139 | 7.2334    |
| 0.866         | 16.0  | 2016 | 0.9311          | 7.4031 | 4.9454 | 6.9449 | 7.2774    |
| 0.866         | 17.0  | 2142 | 0.9328          | 7.3089 | 4.927  | 6.8732 | 7.1792    |
| 0.8521        | 18.0  | 2268 | 0.9271          | 7.287  | 4.9457 | 6.8903 | 7.1785    |
| 0.8521        | 19.0  | 2394 | 0.9268          | 7.2971 | 4.9465 | 6.8599 | 7.1433    |
| 0.8292        | 20.0  | 2520 | 0.9280          | 7.3163 | 4.9526 | 6.8963 | 7.1913    |
| 0.8292        | 21.0  | 2646 | 0.9280          | 7.2896 | 4.9634 | 6.8796 | 7.1637    |
| 0.8278        | 22.0  | 2772 | 0.9261          | 7.3053 | 4.9904 | 6.8909 | 7.1665    |
| 0.8278        | 23.0  | 2898 | 0.9261          | 7.2905 | 4.9755 | 6.8948 | 7.1735    |
| 0.8157        | 24.0  | 3024 | 0.9250          | 7.2759 | 4.9718 | 6.8659 | 7.1682    |
| 0.8157        | 25.0  | 3150 | 0.9256          | 7.2898 | 4.9395 | 6.8631 | 7.1709    |
| 0.8052        | 26.0  | 3276 | 0.9244          | 7.2833 | 4.9821 | 6.8806 | 7.1818    |
| 0.8052        | 27.0  | 3402 | 0.9240          | 7.2708 | 4.9682 | 6.8609 | 7.1558    |
| 0.8055        | 28.0  | 3528 | 0.9250          | 7.2671 | 4.9469 | 6.8412 | 7.1365    |
| 0.8055        | 29.0  | 3654 | 0.9242          | 7.264  | 4.9601 | 6.8631 | 7.1534    |
| 0.8016        | 30.0  | 3780 | 0.9248          | 7.2543 | 4.93   | 6.8455 | 7.1492    |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.1
- Datasets 3.0.1
- Tokenizers 0.20.0