---
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-lecturaFacil
  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-lecturaFacil

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: 0.9271
- Rouge1: 7.3252
- Rouge2: 4.9334
- Rougel: 6.8872
- Rougelsum: 7.1241

## 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  | 1.0321          | 7.3748 | 4.9629 | 6.9138 | 7.2111    |
| 1.1181        | 2.0   | 252  | 1.0070          | 7.4115 | 5.0322 | 6.9997 | 7.2421    |
| 1.1181        | 3.0   | 378  | 0.9969          | 7.3898 | 5.1086 | 6.9923 | 7.231     |
| 1.0383        | 4.0   | 504  | 0.9813          | 7.4184 | 5.0788 | 7.0037 | 7.2501    |
| 1.0383        | 5.0   | 630  | 0.9688          | 7.3659 | 5.039  | 6.9308 | 7.2087    |
| 0.983         | 6.0   | 756  | 0.9622          | 7.3641 | 4.937  | 6.9081 | 7.1724    |
| 0.983         | 7.0   | 882  | 0.9544          | 7.295  | 4.8189 | 6.8761 | 7.1125    |
| 0.951         | 8.0   | 1008 | 0.9511          | 7.3963 | 4.9845 | 6.9474 | 7.2114    |
| 0.951         | 9.0   | 1134 | 0.9465          | 7.3098 | 4.9128 | 6.839  | 7.126     |
| 0.9158        | 10.0  | 1260 | 0.9460          | 7.2637 | 4.8619 | 6.7836 | 7.0688    |
| 0.9158        | 11.0  | 1386 | 0.9419          | 7.2981 | 4.9045 | 6.8633 | 7.0929    |
| 0.8855        | 12.0  | 1512 | 0.9408          | 7.2316 | 4.8556 | 6.8036 | 7.0365    |
| 0.8855        | 13.0  | 1638 | 0.9386          | 7.304  | 4.9343 | 6.8534 | 7.1372    |
| 0.8728        | 14.0  | 1764 | 0.9361          | 7.306  | 4.8842 | 6.8442 | 7.1085    |
| 0.8728        | 15.0  | 1890 | 0.9358          | 7.3659 | 4.9194 | 6.9266 | 7.1365    |
| 0.8454        | 16.0  | 2016 | 0.9322          | 7.3037 | 4.9331 | 6.8693 | 7.1027    |
| 0.8454        | 17.0  | 2142 | 0.9309          | 7.3274 | 5.0087 | 6.933  | 7.149     |
| 0.8327        | 18.0  | 2268 | 0.9297          | 7.3654 | 4.9828 | 6.9368 | 7.1698    |
| 0.8327        | 19.0  | 2394 | 0.9287          | 7.3123 | 4.9036 | 6.8939 | 7.1085    |
| 0.8247        | 20.0  | 2520 | 0.9285          | 7.3767 | 5.0096 | 6.9674 | 7.1994    |
| 0.8247        | 21.0  | 2646 | 0.9280          | 7.3385 | 4.9576 | 6.9177 | 7.1509    |
| 0.8123        | 22.0  | 2772 | 0.9260          | 7.2944 | 4.967  | 6.8986 | 7.1205    |
| 0.8123        | 23.0  | 2898 | 0.9283          | 7.3318 | 4.9947 | 6.9124 | 7.1526    |
| 0.8022        | 24.0  | 3024 | 0.9265          | 7.3281 | 5.0144 | 6.9243 | 7.1457    |
| 0.8022        | 25.0  | 3150 | 0.9267          | 7.3298 | 4.9377 | 6.8935 | 7.122     |
| 0.7902        | 26.0  | 3276 | 0.9285          | 7.3649 | 5.0498 | 6.9359 | 7.1817    |
| 0.7902        | 27.0  | 3402 | 0.9264          | 7.3309 | 4.9617 | 6.9008 | 7.1364    |
| 0.7932        | 28.0  | 3528 | 0.9270          | 7.3137 | 4.9215 | 6.873  | 7.1133    |
| 0.7932        | 29.0  | 3654 | 0.9272          | 7.3203 | 4.9291 | 6.8813 | 7.1206    |
| 0.7849        | 30.0  | 3780 | 0.9271          | 7.3252 | 4.9334 | 6.8872 | 7.1241    |


### Framework versions

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