Created model card.

README.md

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+# roberta-base-frenk-hate
+
+Text classification model based on `classla/bcms-bertic` 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 outperformance was statistically significant.