Created model card.

README.md

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+# sloberta-frenk-hate
+Text classification model based on `EMBEDDIA/sloberta` and fine-tuned on the [FRANK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the slovenian 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": 14,
+        "learning_rate": 1e-5,
+        "train_batch_size": 21,
+        }
+```
+
+## 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|
+|---|---|---|
+|sloberta-frenk-hate|0.7785|0.7764|
+|EMBEDDIA/crosloengual-bert |0.7616|0.7585|
+|xlm-roberta-base |0.686|0.6827|
+
+From recorded accuracies and macro F1 scores p-values were also calculated:
+
+`Sloberta` vs `crosloengual-bert`:
+| test | accuracy p-value | macro F1 p-value|
+| --- | --- | --- |
+|Wilcoxon|0.00781|0.00781|
+|Mann Whithey|0.00163|0.00108|
+|Student t-test |0.000101|3.95e-05|
+
+`Sloberta` vs `xlm-roberta-base`:
+| test | accuracy p-value | macro F1 p-value|
+| --- | --- | --- |
+|Wilcoxon|0.00781|0.00781|
+|Mann Whithey|0.00108|0.00108|
+|Student t-test |9.46e-11|6.94e-11|