Update README.md

README.md

@@ -9,7 +9,7 @@ metrics:
 pipeline_tag: text-classification
 ---
 
-`ArGTClass` is a bloomz based classification model, finetuned to categorize a comprehensive spectrum
+`ArGTClass` is a `bloomz` based classification model, finetuned to categorize a comprehensive spectrum
 of fourteen distinct subjects that are Religion,
 Finance and Economics, Politics, Medical, Cul-
 ture, Sports, Science and Technology, Anthro-
@@ -28,7 +28,7 @@ For more details, check out our [example.com](paper)
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 
 tokenizer = AutoTokenizer.from_pretrained("dru-ac/ArGTClass")
-model = AutoModelForSequenceClassification.from_pretrained("dru-ac/ArGTClass", device_map = 'auto')
+model = AutoModelForSequenceClassification.from_pretrained("dru-ac/ArGTClass")
 
 text = " .قصفت إسرائيل مستشفى المعمداني في مدينة غزة، والذي خلف مئات الشهداء والجرحى"
 
@@ -48,7 +48,7 @@ model = AutoModelForSequenceClassification.from_pretrained("dru-ac/ArGTClass", d
 
 text = " .قصفت إسرائيل مستشفى المعمداني في مدينة غزة، والذي خلف مئات الشهداء والجرحى"
 
-inputs = tokenizer(text, return_tensors= 'pt')
+inputs = tokenizer(text, return_tensors= 'pt').to("cuda")
 outputs = model(**inputs)
 ind = outputs.logits.argmax(dim=-1)[0]
 predicted_class = model.config.id2label[ind.item()]
@@ -69,17 +69,3 @@ text = " .قصفت إسرائيل مستشفى المعمداني في مدين
 classifier(text)
 ```
 
-
-### Pipeline example (GPU)
-
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-tokenizer = AutoTokenizer.from_pretrained("dru-ac/ArGTClass")
-model = AutoModelForSequenceClassification.from_pretrained("dru-ac/ArGTClass", device_map = 'auto')
-
-classifier = pipeline("text-classification", model=model, tokenizer= tokenizer, device="cuda:0")
-
-text = " .قصفت إسرائيل مستشفى المعمداني في مدينة غزة، والذي خلف مئات الشهداء والجرحى"
-
-classifier(text)
-```