---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: robbert2809_lrate10
  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_lrate10

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: 0.3760
- Precision: 0.7615
- Recall: 0.7517
- F1: 0.7566
- Accuracy: 0.8990

## 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: 0.0001
- 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  | 0.4116          | 0.7166    | 0.7104 | 0.7135 | 0.8906   |
| No log        | 2.0   | 236  | 0.3760          | 0.7615    | 0.7517 | 0.7566 | 0.8990   |
| No log        | 3.0   | 354  | 0.4114          | 0.7428    | 0.7692 | 0.7558 | 0.9019   |
| No log        | 4.0   | 472  | 0.4230          | 0.7881    | 0.7844 | 0.7862 | 0.9131   |
| 0.1527        | 5.0   | 590  | 0.4550          | 0.7858    | 0.7716 | 0.7786 | 0.9092   |
| 0.1527        | 6.0   | 708  | 0.4553          | 0.7876    | 0.8019 | 0.7947 | 0.9188   |
| 0.1527        | 7.0   | 826  | 0.4824          | 0.7864    | 0.8001 | 0.7932 | 0.9181   |
| 0.1527        | 8.0   | 944  | 0.4973          | 0.7922    | 0.7978 | 0.7950 | 0.9196   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3