Update README.md

README.md

@@ -57,8 +57,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('antoinelouis/biencoder-antoinelouis-biencoder-camembert-base-mmarcoFRFR-bsard')
-model = AutoModel.from_pretrained('antoinelouis/biencoder-antoinelouis-biencoder-camembert-base-mmarcoFRFR-bsard')
+tokenizer = AutoTokenizer.from_pretrained('maastrichtlawtech/camembert-base-lleqa')
+model = AutoModel.from_pretrained('maastrichtlawtech/camembert-base-lleqa')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -79,7 +79,7 @@ We evaluate the model on the test set of LLeQA, which consists of 195 legal ques
 
 |   MRR@10 |   NDCG@10 |   MAP@10 |   R@10 |   R@100 |   R@500 |
 |---------:|----------:|---------:|-------:|--------:|--------:|
-|    19.03 |     14.36 |    10.77 |  15.95 |   34.12 |   52.26 |
+|    36.55 |     39.27 |    30.64 |  58.27 |   82.43 |   92.41 |
 
 ## Training
 ***