metadata
license: apache-2.0
tags:
- generated_from_trainer
pipeline_tag: text2text-generation
inference:
parameters:
max_length: 256
num_beams: 4
length_penalty: 1.5
no_repeat_ngram_size: 3
early_stopping: true
base_model: yhavinga/t5-base-dutch
model-index:
- name: t5-end2end-questions-generation-dutch
results: []
t5-end2end-questions-generation-dutch
This model is a fine-tuned version of yhavinga/t5-base-dutch on a Google translated version of SQUAD 1.1 found here: https://www.kaggle.com/datasets/michelvanheijningen/squad1-dutch.
The code used to finetune the model is largely based on the work by Thomas Simonini. You can find his English model here and his Google colab tutorial here
It achieves the following results on the evaluation set:
- Loss: 1.6546
Model description
This is my first model ever and still a work in progress ;)
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.6528 | 0.34 | 100 | 1.9249 |
| 1.964 | 0.68 | 200 | 1.7897 |
| 1.8695 | 1.02 | 300 | 1.7554 |
| 1.7922 | 1.35 | 400 | 1.7270 |
| 1.7747 | 1.69 | 500 | 1.7054 |
| 1.7473 | 2.03 | 600 | 1.7019 |
| 1.697 | 2.37 | 700 | 1.6868 |
| 1.6848 | 2.71 | 800 | 1.6810 |
| 1.6756 | 3.05 | 900 | 1.6779 |
| 1.6282 | 3.39 | 1000 | 1.6712 |
| 1.6285 | 3.73 | 1100 | 1.6626 |
| 1.6161 | 4.06 | 1200 | 1.6616 |
| 1.5887 | 4.4 | 1300 | 1.6588 |
| 1.5877 | 4.74 | 1400 | 1.6583 |
| 1.5723 | 5.08 | 1500 | 1.6560 |
| 1.5545 | 5.42 | 1600 | 1.6550 |
| 1.5415 | 5.76 | 1700 | 1.6540 |
| 1.5509 | 6.1 | 1800 | 1.6541 |
| 1.5326 | 6.44 | 1900 | 1.6539 |
| 1.5268 | 6.77 | 2000 | 1.6546 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2