---
base_model: yhavinga/ul2-large-dutch
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: ul2-large-dutch-finetuned-oba-book-search
  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. -->

# ul2-large-dutch-finetuned-oba-book-search

This model is a fine-tuned version of [yhavinga/ul2-large-dutch](https://huggingface.co/yhavinga/ul2-large-dutch) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4663
- Top-5-accuracy: 0.4179

## 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: 0.03
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Top-5-accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------------:|
| 6.6127        | 0.0170 | 200   | 4.9957          | 0.0199         |
| 6.3546        | 0.0339 | 400   | 4.6613          | 0.0796         |
| 6.3036        | 0.0509 | 600   | 4.7338          | 0.0            |
| 6.0421        | 0.0678 | 800   | 4.7298          | 0.0            |
| 6.0575        | 0.0848 | 1000  | 4.5229          | 0.0597         |
| 5.9802        | 0.1017 | 1200  | 4.6572          | 0.0398         |
| 5.8962        | 0.1187 | 1400  | 4.5695          | 0.0199         |
| 5.7797        | 0.1357 | 1600  | 4.5181          | 0.0199         |
| 5.7508        | 0.1526 | 1800  | 4.5089          | 0.0995         |
| 5.6505        | 0.1696 | 2000  | 4.5137          | 0.0199         |
| 5.705         | 0.1865 | 2200  | 4.4988          | 0.0796         |
| 5.6986        | 0.2035 | 2400  | 4.4908          | 0.0199         |
| 5.6822        | 0.2205 | 2600  | 4.4318          | 0.0199         |
| 5.6889        | 0.2374 | 2800  | 4.5502          | 0.0199         |
| 5.674         | 0.2544 | 3000  | 4.4749          | 0.0199         |
| 5.682         | 0.2713 | 3200  | 4.5109          | 0.0199         |
| 5.6252        | 0.2883 | 3400  | 4.5060          | 0.0796         |
| 5.4972        | 0.3052 | 3600  | 4.4417          | 0.1194         |
| 5.478         | 0.3222 | 3800  | 4.4351          | 0.0597         |
| 5.5038        | 0.3392 | 4000  | 4.4616          | 0.0            |
| 5.6091        | 0.3561 | 4200  | 4.4631          | 0.0995         |
| 5.4895        | 0.3731 | 4400  | 4.4339          | 0.0796         |
| 5.6013        | 0.3900 | 4600  | 4.4692          | 0.0995         |
| 5.4743        | 0.4070 | 4800  | 4.5731          | 0.0796         |
| 5.4131        | 0.4239 | 5000  | 4.5185          | 0.0995         |
| 5.4779        | 0.4409 | 5200  | 4.4901          | 0.0995         |
| 5.5093        | 0.4579 | 5400  | 4.5193          | 0.0995         |
| 5.527         | 0.4748 | 5600  | 4.5322          | 0.1194         |
| 5.5443        | 0.4918 | 5800  | 4.5358          | 0.0796         |
| 5.557         | 0.5087 | 6000  | 4.5574          | 0.0995         |
| 5.5324        | 0.5257 | 6200  | 4.4957          | 0.2786         |
| 5.4958        | 0.5426 | 6400  | 4.5618          | 0.1592         |
| 5.4376        | 0.5596 | 6600  | 4.4751          | 0.1194         |
| 5.5136        | 0.5766 | 6800  | 4.4994          | 0.1393         |
| 5.4284        | 0.5935 | 7000  | 4.5029          | 0.2587         |
| 5.4333        | 0.6105 | 7200  | 4.4864          | 0.2189         |
| 5.3516        | 0.6274 | 7400  | 4.5141          | 0.1990         |
| 5.4294        | 0.6444 | 7600  | 4.4527          | 0.1990         |
| 5.4383        | 0.6614 | 7800  | 4.4698          | 0.0199         |
| 5.3333        | 0.6783 | 8000  | 4.4169          | 0.2189         |
| 5.3708        | 0.6953 | 8200  | 4.4541          | 0.2587         |
| 5.3639        | 0.7122 | 8400  | 4.4613          | 0.2587         |
| 5.3746        | 0.7292 | 8600  | 4.4467          | 0.2786         |
| 5.3916        | 0.7461 | 8800  | 4.4134          | 0.4378         |
| 5.3416        | 0.7631 | 9000  | 4.4772          | 0.4179         |
| 5.3148        | 0.7801 | 9200  | 4.4603          | 0.3980         |
| 5.3646        | 0.7970 | 9400  | 4.4700          | 0.3582         |
| 5.2917        | 0.8140 | 9600  | 4.4439          | 0.3781         |
| 5.386         | 0.8309 | 9800  | 4.4418          | 0.3582         |
| 5.3907        | 0.8479 | 10000 | 4.4431          | 0.4179         |
| 5.4036        | 0.8648 | 10200 | 4.4557          | 0.3980         |
| 5.3439        | 0.8818 | 10400 | 4.4373          | 0.4179         |
| 5.2866        | 0.8988 | 10600 | 4.4616          | 0.4179         |
| 5.3447        | 0.9157 | 10800 | 4.4669          | 0.3980         |
| 5.3031        | 0.9327 | 11000 | 4.4639          | 0.4179         |
| 5.4083        | 0.9496 | 11200 | 4.4726          | 0.4179         |
| 5.2586        | 0.9666 | 11400 | 4.4771          | 0.3980         |
| 5.3988        | 0.9836 | 11600 | 4.4663          | 0.4179         |


### Framework versions

- PEFT 0.11.0
- Transformers 4.44.2
- Pytorch 1.13.0+cu116
- Datasets 3.0.0
- Tokenizers 0.19.1