Removed model args from use example

README.md

@@ -31,12 +31,12 @@ model_args = {
 
 The same pipeline was run with two other transformer models and `fasttext` for comparison. 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|
-|---|---|---|
-|bcms-bertic-frenk-hate|0.8313|0.8219|
-|EMBEDDIA/crosloengual-bert |0.8054|0.796|
-|xlm-roberta-base |0.7175|0.7049|
-|fasttext|0.771 |0.754 |
+| model                      | average accuracy | average macro F1 |
+|----------------------------|------------------|------------------|
+| bcms-bertic-frenk-hate     | 0.8313           | 0.8219           |
+| EMBEDDIA/crosloengual-bert | 0.8054           | 0.796            |
+| xlm-roberta-base           | 0.7175           | 0.7049           |
+| fasttext                   | 0.771            | 0.754            |
 
 
 
@@ -44,19 +44,19 @@ From recorded accuracies and macro F1 scores p-values were also calculated:
 
 Comparison with `crosloengual-bert`:
 
-| test | accuracy p-value | macro F1 p-value|
-| --- | --- | --- |
-|Wilcoxon|0.00781|0.00781|
-|Mann Whithney|0.00108|0.00108|
-|Student t-test |2.43e-10 |1.27e-10|
+| test           | accuracy p-value | macro F1 p-value |
+|----------------|------------------|------------------|
+| Wilcoxon       | 0.00781          | 0.00781          |
+| Mann Whithney  | 0.00108          | 0.00108          |
+| Student t-test | 2.43e-10         | 1.27e-10         |
 
 Comparison with `xlm-roberta-base`:
 
-| test | accuracy p-value | macro F1 p-value|
-| --- | --- | --- |
-|Wilcoxon|0.00781|0.00781|
-|Mann Whithney|0.00107|0.00108|
-|Student t-test |4.83e-11 | 5.61e-11 |
+| test           | accuracy p-value | macro F1 p-value |
+|----------------|------------------|------------------|
+| Wilcoxon       | 0.00781          | 0.00781          |
+| Mann Whithney  | 0.00107          | 0.00108          |
+| Student t-test | 4.83e-11         | 5.61e-11         |
 
 
 
@@ -64,14 +64,10 @@ Comparison with `xlm-roberta-base`:
 
 ```python
 from simpletransformers.classification import ClassificationModel
-model_args = {
-        "num_train_epochs": 12,
-        "learning_rate": 1e-5,
-        "train_batch_size": 74}
 
 model = ClassificationModel(
     "bert", "5roop/bcms-bertic-frenk-hate", use_cuda=True,
-    args=model_args
+
     
 )