---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pos_final_mono_nl
  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. -->

# pos_final_mono_nl

This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1115
- Precision: 0.9783
- Recall: 0.9784
- F1: 0.9783
- Accuracy: 0.9791

## 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: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 69   | 3.7703          | 0.2597    | 0.1252 | 0.1689 | 0.2575   |
| No log        | 2.0   | 138  | 1.0148          | 0.8058    | 0.8008 | 0.8033 | 0.8066   |
| No log        | 3.0   | 207  | 0.3402          | 0.9302    | 0.9278 | 0.9290 | 0.9299   |
| No log        | 4.0   | 276  | 0.2016          | 0.9559    | 0.9551 | 0.9555 | 0.9561   |
| No log        | 5.0   | 345  | 0.1486          | 0.9643    | 0.9638 | 0.9641 | 0.9648   |
| No log        | 6.0   | 414  | 0.1206          | 0.9697    | 0.9696 | 0.9697 | 0.9702   |
| No log        | 7.0   | 483  | 0.1063          | 0.9720    | 0.9719 | 0.9720 | 0.9727   |
| 1.2192        | 8.0   | 552  | 0.0983          | 0.9734    | 0.9735 | 0.9735 | 0.9742   |
| 1.2192        | 9.0   | 621  | 0.0947          | 0.9746    | 0.9747 | 0.9746 | 0.9754   |
| 1.2192        | 10.0  | 690  | 0.0913          | 0.9753    | 0.9755 | 0.9754 | 0.9761   |
| 1.2192        | 11.0  | 759  | 0.0885          | 0.9761    | 0.9763 | 0.9762 | 0.9770   |
| 1.2192        | 12.0  | 828  | 0.0877          | 0.9764    | 0.9765 | 0.9764 | 0.9772   |
| 1.2192        | 13.0  | 897  | 0.0878          | 0.9767    | 0.9769 | 0.9768 | 0.9775   |
| 1.2192        | 14.0  | 966  | 0.0873          | 0.9767    | 0.9769 | 0.9768 | 0.9776   |
| 0.0688        | 15.0  | 1035 | 0.0877          | 0.9771    | 0.9773 | 0.9772 | 0.9779   |
| 0.0688        | 16.0  | 1104 | 0.0878          | 0.9773    | 0.9774 | 0.9773 | 0.9781   |
| 0.0688        | 17.0  | 1173 | 0.0897          | 0.9772    | 0.9773 | 0.9773 | 0.9781   |
| 0.0688        | 18.0  | 1242 | 0.0909          | 0.9775    | 0.9776 | 0.9776 | 0.9783   |
| 0.0688        | 19.0  | 1311 | 0.0917          | 0.9776    | 0.9778 | 0.9777 | 0.9785   |
| 0.0688        | 20.0  | 1380 | 0.0924          | 0.9778    | 0.9780 | 0.9779 | 0.9787   |
| 0.0688        | 21.0  | 1449 | 0.0949          | 0.9777    | 0.9779 | 0.9778 | 0.9785   |
| 0.0366        | 22.0  | 1518 | 0.0956          | 0.9776    | 0.9777 | 0.9777 | 0.9784   |
| 0.0366        | 23.0  | 1587 | 0.0962          | 0.9778    | 0.9780 | 0.9779 | 0.9786   |
| 0.0366        | 24.0  | 1656 | 0.0992          | 0.9777    | 0.9780 | 0.9779 | 0.9786   |
| 0.0366        | 25.0  | 1725 | 0.0999          | 0.9779    | 0.9781 | 0.9780 | 0.9787   |
| 0.0366        | 26.0  | 1794 | 0.1007          | 0.9780    | 0.9782 | 0.9781 | 0.9789   |
| 0.0366        | 27.0  | 1863 | 0.1022          | 0.9781    | 0.9782 | 0.9782 | 0.9789   |
| 0.0366        | 28.0  | 1932 | 0.1030          | 0.9781    | 0.9783 | 0.9782 | 0.9790   |
| 0.0226        | 29.0  | 2001 | 0.1055          | 0.9781    | 0.9782 | 0.9781 | 0.9789   |
| 0.0226        | 30.0  | 2070 | 0.1057          | 0.9780    | 0.9782 | 0.9781 | 0.9789   |
| 0.0226        | 31.0  | 2139 | 0.1067          | 0.9780    | 0.9781 | 0.9780 | 0.9788   |
| 0.0226        | 32.0  | 2208 | 0.1077          | 0.9780    | 0.9782 | 0.9781 | 0.9789   |
| 0.0226        | 33.0  | 2277 | 0.1085          | 0.9780    | 0.9781 | 0.9781 | 0.9789   |
| 0.0226        | 34.0  | 2346 | 0.1094          | 0.9781    | 0.9782 | 0.9781 | 0.9789   |
| 0.0226        | 35.0  | 2415 | 0.1095          | 0.9783    | 0.9784 | 0.9783 | 0.9791   |
| 0.0226        | 36.0  | 2484 | 0.1101          | 0.9780    | 0.9782 | 0.9781 | 0.9789   |
| 0.0159        | 37.0  | 2553 | 0.1114          | 0.9782    | 0.9784 | 0.9783 | 0.9791   |
| 0.0159        | 38.0  | 2622 | 0.1111          | 0.9782    | 0.9784 | 0.9783 | 0.9791   |
| 0.0159        | 39.0  | 2691 | 0.1114          | 0.9782    | 0.9784 | 0.9783 | 0.9791   |
| 0.0159        | 40.0  | 2760 | 0.1115          | 0.9783    | 0.9784 | 0.9783 | 0.9791   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.0
- Datasets 2.18.0
- Tokenizers 0.13.2