L-\d -> L\d

README.md

@@ -14,8 +14,8 @@ The model can be used for Information Retrieval: Given a query, encode the query
 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/ms-marco-TinyBERT-L2-v2')
+tokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-TinyBERT-L2-v2')
 
 features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'],  padding=True, truncation=True, return_tensors="pt")
 
@@ -31,7 +31,8 @@ with torch.no_grad():
 The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
 ```python
 from sentence_transformers import CrossEncoder
-model = CrossEncoder('model_name', max_length=512)
+
+model = CrossEncoder('cross-encoder/ms-marco-TinyBERT-L2-v2', max_length=512)
 scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
 ```
 
@@ -43,15 +44,15 @@ In the following table, we provide various pre-trained Cross-Encoders together w
 | Model-Name        | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev)  | Docs / Sec |
 | ------------- |:-------------| -----| --- | 
 | **Version 2 models** | | | 
-| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
-| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
-| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
-| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
-| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
+| cross-encoder/ms-marco-TinyBERT-L2-v2 | 69.84 | 32.56 | 9000
+| cross-encoder/ms-marco-MiniLM-L2-v2 | 71.01 | 34.85 | 4100
+| cross-encoder/ms-marco-MiniLM-L4-v2 | 73.04 | 37.70 | 2500
+| cross-encoder/ms-marco-MiniLM-L6-v2 | 74.30 | 39.01 | 1800
+| cross-encoder/ms-marco-MiniLM-L12-v2 | 74.31 | 39.02 | 960
 | **Version 1 models** | | | 
-| cross-encoder/ms-marco-TinyBERT-L-2  | 67.43 | 30.15  | 9000
-| cross-encoder/ms-marco-TinyBERT-L-4  | 68.09 | 34.50  | 2900
-| cross-encoder/ms-marco-TinyBERT-L-6 |  69.57 | 36.13  | 680
+| cross-encoder/ms-marco-TinyBERT-L2  | 67.43 | 30.15  | 9000
+| cross-encoder/ms-marco-TinyBERT-L4  | 68.09 | 34.50  | 2900
+| cross-encoder/ms-marco-TinyBERT-L6 |  69.57 | 36.13  | 680
 | cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
 | **Other models** | | | 
 | nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900