Update README.md

README.md

@@ -3,4 +3,44 @@ This gBert-base model was finetuned on a sentiment prediction task with tweets f
 This model was trained on ~30.000 annotated tweets in German language on its sentiment. It can predict tweets as negative, positive or neutral. It achieved an accuracy of 93% on the specific dataset.
 
 ## Model Implementation
-You can implement this model for example with Simpletransformers. First you have to unpack the file.
+You can implement this model for example with Simpletransformers. First you have to unpack the file.
+
+    def unpack_model(model_name=''):
+      tar = tarfile.open(f"{model_name}.tar.gz", "r:gz")
+      tar.extractall()
+      tar.close()
+      
+ The hyperparameter were defined as follows:
+     train_args ={"reprocess_input_data": True,
+             "fp16":False,
+             "num_train_epochs": 4,
+             "overwrite_output_dir":True,
+             "train_batch_size": 32,
+             "eval_batch_size": 32}
+             
+  Now create the model:
+    unpack_model(YOUR_DOWNLOADED_FILE_HERE)
+
+    model = ClassificationModel(
+        "bert", "content/outputs/",
+        num_labels= 3,
+        args=train_args
+    )
+    
+In this case for the output:
+- 0 = positive
+- 1 = negative
+- 2 = neutral
+    
+Example for a positive prediction:
+    model.predict(["Das ist gut! Wir danken dir."])
+    ([0], array([[ 2.06561327, -3.57908797,  1.5340755 ]]))
+
+Example for a negative prediction:
+    model.predict(["Ich hasse dich!"])
+    ([1], array([[-3.50486898,  4.29590368, -0.9000684 ]]))
+
+Example for a neutral prediction:
+    model.predict(["Heute ist Sonntag."])
+    ([2], array([[-2.94458342, -2.91875601,  4.94414234]]))
+