---
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: 4.2516
- Top-5-accuracy: 3.5577

## 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: 10

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Top-5-accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------------:|
| 7.6676        | 0.1729 | 200   | 5.1849          | 0.0            |
| 6.7322        | 0.3457 | 400   | 4.6030          | 0.0680         |
| 6.3351        | 0.5186 | 600   | 4.5872          | 0.0            |
| 6.1233        | 0.6914 | 800   | 4.5459          | 0.0453         |
| 6.0233        | 0.8643 | 1000  | 4.6328          | 0.0453         |
| 5.9119        | 1.0372 | 1200  | 4.6239          | 0.0            |
| 5.7683        | 1.2100 | 1400  | 4.5214          | 0.0680         |
| 5.8039        | 1.3829 | 1600  | 4.4935          | 0.0680         |
| 5.7866        | 1.5557 | 1800  | 4.4634          | 0.1586         |
| 5.6501        | 1.7286 | 2000  | 4.5163          | 0.1360         |
| 5.5949        | 1.9015 | 2200  | 4.4098          | 0.0906         |
| 5.5334        | 2.0743 | 2400  | 4.4172          | 0.0906         |
| 5.5175        | 2.2472 | 2600  | 4.4015          | 0.1586         |
| 5.5198        | 2.4201 | 2800  | 4.4079          | 0.1586         |
| 5.4601        | 2.5929 | 3000  | 4.4179          | 0.3172         |
| 5.5095        | 2.7658 | 3200  | 4.4219          | 0.3626         |
| 5.3924        | 2.9386 | 3400  | 4.3597          | 0.3399         |
| 5.4045        | 3.1115 | 3600  | 4.3392          | 0.6798         |
| 5.347         | 3.2844 | 3800  | 4.3186          | 0.5892         |
| 5.3494        | 3.4572 | 4000  | 4.3192          | 0.6798         |
| 5.3725        | 3.6301 | 4200  | 4.3734          | 0.3852         |
| 5.3407        | 3.8029 | 4400  | 4.3198          | 1.2690         |
| 5.3895        | 3.9758 | 4600  | 4.3233          | 0.7478         |
| 5.2923        | 4.1487 | 4800  | 4.3004          | 0.7931         |
| 5.3286        | 4.3215 | 5000  | 4.3195          | 1.2010         |
| 5.2082        | 4.4944 | 5200  | 4.3138          | 1.6089         |
| 5.2735        | 4.6672 | 5400  | 4.3083          | 1.9261         |
| 5.2474        | 4.8401 | 5600  | 4.2709          | 2.1754         |
| 5.2209        | 5.0130 | 5800  | 4.2812          | 2.2660         |
| 5.1823        | 5.1858 | 6000  | 4.2709          | 2.7646         |
| 5.2629        | 5.3587 | 6200  | 4.2837          | 2.2207         |
| 5.1891        | 5.5315 | 6400  | 4.3217          | 2.3114         |
| 5.2377        | 5.7044 | 6600  | 4.2810          | 2.6966         |
| 5.1904        | 5.8773 | 6800  | 4.3004          | 2.4247         |
| 5.1698        | 6.0501 | 7000  | 4.2687          | 2.6513         |
| 5.2109        | 6.2230 | 7200  | 4.2725          | 2.9912         |
| 5.2525        | 6.3959 | 7400  | 4.3036          | 2.8325         |
| 5.1317        | 6.5687 | 7600  | 4.2520          | 3.3311         |
| 5.1662        | 6.7416 | 7800  | 4.2796          | 2.6059         |
| 5.1612        | 6.9144 | 8000  | 4.2556          | 2.9005         |
| 5.223         | 7.0873 | 8200  | 4.2581          | 3.2857         |
| 5.1323        | 7.2602 | 8400  | 4.2629          | 3.3084         |
| 5.1419        | 7.4330 | 8600  | 4.2433          | 3.3764         |
| 5.1321        | 7.6059 | 8800  | 4.2467          | 3.4670         |
| 5.083         | 7.7787 | 9000  | 4.2507          | 3.4670         |
| 5.1572        | 7.9516 | 9200  | 4.2737          | 3.3764         |
| 5.1645        | 8.1245 | 9400  | 4.2425          | 3.5123         |
| 5.1241        | 8.2973 | 9600  | 4.2509          | 3.3990         |
| 5.2118        | 8.4702 | 9800  | 4.2655          | 3.3311         |
| 5.1387        | 8.6430 | 10000 | 4.2600          | 3.4670         |
| 5.1755        | 8.8159 | 10200 | 4.2686          | 3.3764         |
| 5.1629        | 8.9888 | 10400 | 4.2540          | 3.4897         |
| 5.1535        | 9.1616 | 10600 | 4.2442          | 3.6257         |
| 5.2023        | 9.3345 | 10800 | 4.2558          | 3.4897         |
| 5.087         | 9.5073 | 11000 | 4.2468          | 3.5803         |
| 5.0679        | 9.6802 | 11200 | 4.2485          | 3.5803         |
| 5.1546        | 9.8531 | 11400 | 4.2516          | 3.5577         |


### Framework versions

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