NEGATIEF NEUTRAAL POSITIEF
---
license: mit
tags:
- generated_from_trainer
datasets:
- dutch_social
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: robbert-twitter-sentiment-tokenized
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: dutch_social
      type: dutch_social
      args: dutch_social
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.814
    - name: F1
      type: f1
      value: 0.8132800039281481
    - name: Precision
      type: precision
      value: 0.8131073640029836
    - name: Recall
      type: recall
      value: 0.814
---

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# robbert-twitter-sentiment-tokenized

This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5473
- Accuracy: 0.814
- F1: 0.8133
- Precision: 0.8131
- Recall: 0.814

## 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: 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6895        | 1.0   | 282  | 0.6307          | 0.7433   | 0.7442 | 0.7500    | 0.7433 |
| 0.4948        | 2.0   | 564  | 0.5189          | 0.8053   | 0.8062 | 0.8081    | 0.8053 |
| 0.2642        | 3.0   | 846  | 0.5473          | 0.814    | 0.8133 | 0.8131    | 0.814  |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6
