# roberta-base-frenk-hate

Text classification model based on `roberta-base` and fine-tuned on the [FRANK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the English subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable).

## Fine-tuning hyperparameters

Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are:

```python

model_args = {
        "num_train_epochs": 6,
        "learning_rate": 3e-6,
        "train_batch_size": 69}
```

## Performance

The same pipeline was run with two other models and with the same dataset. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed.

| model | average accuracy | average macro F1|
|---|---|---|
|roberta-base-frenk-hate|0.7915|0.7785|
|xlm-roberta-large |0.7904|0.77876|
|xlm-roberta-base |0.7577|0.7402|
|distilbert-base-uncased-finetuned-sst-2-english|0.7201|0.69862|



From recorded accuracies and macro F1 scores p-values were also calculated:

Comparison with `xlm-roberta-base`:

| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.00781|0.00781|
|Mann Whithney U-test|0.00108|0.00108|
|Student t-test | 1.35e-08 | 1.05e-07|

Comparison with `distilbert-base-uncased-finetuned-sst-2-english`:

| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.00781|0.00781|
|Mann Whithney U-test|0.00108|0.00108|
|Student t-test | 1.33e-12 	 | 3.03e-12|

Comparison with `xlm-roberta-large` yielded inconclusive results; whereas accuracy was outperformed by this model, the macro F1 score was not. Neither metric allowed for statistically significant conclusions about which model might be better.

## Use examples

```python
from simpletransformers.classification import ClassificationModel
model_args = {
        "num_train_epochs": 6,
        "learning_rate": 3e-6,
        "train_batch_size": 69}

model = ClassificationModel(
    "roberta", "5roop/roberta-base-frenk-hate", use_cuda=True,
    args=model_args
    
)

predictions, logit_output = model.predict(["Build the wall", 
                                        "Build the wall of trust"]
                                        )
predictions
### Output:
### array([1, 0])
```