Update README.md

README.md

@@ -78,7 +78,7 @@ classifier(text)
 
 Here is how we can use the model to get the features of a given text in PyTorch:
 ```python
-!pip install transformers pytorch
+!pip install transformers torch
 
 from transformers import AutoTokenizer
 from transformers import AutoModelForSequenceClassification
@@ -102,7 +102,7 @@ def tokenize_function(examples):
 
 
 # predicting with the model
-word_i_want_to_predict = "राजनीतिक स्थिरता नहुँदा विकास निर्माणले गति लिन सकेन"
+sequence_i_want_to_predict = "राजनीतिक स्थिरता नहुँदा विकास निर्माणले गति लिन सकेन"
 
 # initializing our labels
 label_list = [
@@ -117,7 +117,7 @@ label_list = [
                 "tourism"
 ]
 
-batch = tokenizer(word_i_want_to_predict, padding=True, truncation=True, max_length=512, return_tensors='pt')
+batch = tokenizer(sequence_i_want_to_predict, padding=True, truncation=True, max_length=512, return_tensors='pt')
 
 with torch.no_grad():
     outputs = model(**batch)
@@ -130,8 +130,6 @@ print(f"The sequence: \n\n {word_i_want_to_predict} \n\n is predicted to be of n
 ## Training data
 This model is trained on 50,945 rows of Nepali language news grouped [dataset](https://www.kaggle.com/competitions/text-it-meet-22/data?select=train.csv) found on Kaggle which was also used in IT Meet 2022 Text challenge.
 
-## 
-
 
 ## Framework versions
 - Transformers 4.20.1