Create README.md

README.md

@@ -0,0 +1,45 @@
+---
+language:
+- de
+---
+
+library_name: transformers
+tags:
+- Text Classification
+- Pytorch
+- Discourse Classification
+- Roberta
+---
+# Roberta for German Discourse Classification
+
+This is a xlm Roberta model finetuned on a German Discourse dataset of 60 discourses having a total over 10k sentences.
+
+
+## How to use the model
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+def get_label(sentence):
+    vectors = tokenizer(sentence, return_tensors='pt').to(device)
+    outputs = bert_model(**vectors).logits
+    probs = torch.nn.functional.softmax(outputs, dim = 1)[0]
+    bert_dict = {}
+    keys = ['Externalization', 'Elicitation', 'Conflict', 'Acceptence', 'Integration', 'None']
+    for i in range(len(keys)):
+        bert_dict[keys[i]] = round(probs[i].item(), 3)
+    return bert_dict
+
+MODEL_NAME = 'RashidNLP/Roberta-German-Discourse'
+MODEL_DIR = 'model'
+CHECKPOINT_DIR = 'checkpoints'
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+OUTPUTS = 6
+
+bert_model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels = OUTPUTS).to(device)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+get_label("Gehst du zum Oktoberfest?")
+
+```