Update README.md

README.md

@@ -1,6 +1,7 @@
 ---
 datasets:
 - ElKulako/stocktwits-crypto
+language: english
 
 ---
 
@@ -15,15 +16,15 @@ CryptoBERT was trained with a max sequence length of 128. Technically, it can ha
 
 # Classification Example
 ```python
->>> from transformers import TextClassificationPipeline, AutoModelForSequenceClassification, AutoTokenizer
->>> from datasets import load_dataset
->>> dataset_name = "ElKulako/stocktwits-crypto"
->>> dataset = load_dataset(dataset_name)
->>> model_name = "ElKulako/cryptobert"
->>> tokenizer_ = AutoTokenizer.from_pretrained(model_name, use_fast=True)
->>> model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels = 3)
->>> pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, batch_size=64, max_length=64, truncation=True, padding = 'max_length')
->>> preds = pipe(df_posts)
+from transformers import TextClassificationPipeline, AutoModelForSequenceClassification, AutoTokenizer
+from datasets import load_dataset
+dataset_name = "ElKulako/stocktwits-crypto"
+dataset = load_dataset(dataset_name)
+model_name = "ElKulako/cryptobert"
+tokenizer_ = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels = 3)
+pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, batch_size=64, max_length=64, truncation=True, padding = 'max_length')
+preds = pipe(df_posts)
 
 
 ```