up

README.md

@@ -24,7 +24,7 @@ For evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.s
 Pre-trained models can be used like this:
 ```python
 from sentence_transformers import CrossEncoder
-model = CrossEncoder('model_name')
+model = CrossEncoder('cross-encoder/nli-roberta-base')
 scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
 
 #Convert scores to labels
@@ -38,8 +38,8 @@ You can use the model also directly with Transformers library (without SentenceT
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-model = AutoModelForSequenceClassification.from_pretrained('model_name')
-tokenizer = AutoTokenizer.from_pretrained('model_name')
+model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-roberta-base')
+tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-roberta-base')
 
 features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'],  padding=True, truncation=True, return_tensors="pt")
 
@@ -53,7 +53,7 @@ with torch.no_grad():
 
 ## Zero-Shot Classification
 This model can also be used for zero-shot-classification:
-```
+```python
 from transformers import pipeline
 
 classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-roberta-base')