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robbert-twitter-sentiment-custom

Text Classification
Transformers
PyTorch
roberta
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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robbert-twitter-sentiment-custom
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---
license: mit
tags:
- generated_from_trainer
datasets:
- dutch_social
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: robbert-twitter-sentiment-custom
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: dutch_social
      type: dutch_social
      args: dutch_social
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.788
    - name: F1
      type: f1
      value: 0.7878005279207152
    - name: Precision
      type: precision
      value: 0.7877102066609215
    - name: Recall
      type: recall
      value: 0.788
---

<!-- 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. -->

# robbert-twitter-sentiment-custom

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: 0.6656
- Accuracy: 0.788
- F1: 0.7878
- Precision: 0.7877
- Recall: 0.788

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8287        | 1.0   | 282  | 0.7178          | 0.7007   | 0.6958 | 0.6973    | 0.7007 |
| 0.4339        | 2.0   | 564  | 0.5873          | 0.7667   | 0.7668 | 0.7681    | 0.7667 |
| 0.2045        | 3.0   | 846  | 0.6656          | 0.788    | 0.7878 | 0.7877    | 0.788  |


### Framework versions

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