---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: robbert2809_lrate7.5
  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. -->

# robbert2809_lrate7.5

This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3477
- Precision: 0.7368
- Recall: 0.7716
- F1: 0.7538
- Accuracy: 0.9017

## 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: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 118  | 0.3855          | 0.6721    | 0.6533 | 0.6625 | 0.8795   |
| No log        | 2.0   | 236  | 0.3511          | 0.6921    | 0.7284 | 0.7098 | 0.8880   |
| No log        | 3.0   | 354  | 0.3477          | 0.7368    | 0.7716 | 0.7538 | 0.9017   |
| No log        | 4.0   | 472  | 0.4055          | 0.7489    | 0.7628 | 0.7558 | 0.9019   |
| 0.3159        | 5.0   | 590  | 0.3930          | 0.7506    | 0.7488 | 0.7497 | 0.9035   |
| 0.3159        | 6.0   | 708  | 0.4131          | 0.7684    | 0.7716 | 0.7700 | 0.9082   |
| 0.3159        | 7.0   | 826  | 0.4382          | 0.7714    | 0.7786 | 0.7749 | 0.9093   |
| 0.3159        | 8.0   | 944  | 0.4478          | 0.7817    | 0.7698 | 0.7757 | 0.9097   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3