Update README.md

README.md

@@ -33,7 +33,7 @@ Then you can use the model like this:
 from sentence_transformers import CrossEncoder
 pairs = [('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')]
 
-model = CrossEncoder('crossencoder-mMiniLMv2-L6-H384-distilled-from-XLMR-Large-mmarcoFR')
+model = CrossEncoder('antoinelouis/crossencoder-mMiniLMv2-L6-H384-distilled-from-XLMR-Large-mmarcoFR')
 scores = model.predict(pairs)
 print(scores)
 ```
@@ -46,8 +46,8 @@ Without [sentence-transformers](https://www.SBERT.net), you can use the model as
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-model = AutoModelForSequenceClassification.from_pretrained('crossencoder-mMiniLMv2-L6-H384-distilled-from-XLMR-Large-mmarcoFR')
-tokenizer = AutoTokenizer.from_pretrained('crossencoder-mMiniLMv2-L6-H384-distilled-from-XLMR-Large-mmarcoFR')
+model = AutoModelForSequenceClassification.from_pretrained('antoinelouis/crossencoder-mMiniLMv2-L6-H384-distilled-from-XLMR-Large-mmarcoFR')
+tokenizer = AutoTokenizer.from_pretrained('antoinelouis/crossencoder-mMiniLMv2-L6-H384-distilled-from-XLMR-Large-mmarcoFR')
 
 pairs = [('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')]
 features = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt')
@@ -69,7 +69,7 @@ Below, we compare the model performance with other cross-encoder models fine-tun
 |---:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------:|-------------:|---------:|------------:|------------:|------------:|-------------:|
 |  1 | [crossencoder-camembert-base-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-camembert-base-mmarcoFR)                                                                       | 443MB |        35.65 |    50.44 |       82.95 |       91.50 |       96.80 |        98.80 |
 |  2 | [crossencoder-mMiniLMv2-L12-H384-distilled-from-XLMR-Large-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-mMiniLMv2-L12-H384-distilled-from-XLMR-Large-mmarcoFR)           | 471MB |        34.37 |    51.01 |       82.23 |       90.60 |       96.45 |        98.40 |
-|  3 | [crossencoder-mmarcoFR-mMiniLMv2-L12-H384-v1-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-mmarcoFR-mMiniLMv2-L12-H384-v1-mmarcoFR)                                       | 471MB |        34.22 |    49.20 |       81.70 |       90.90 |       97.10 |        98.90 |
+|  3 | [crossencoder-mMiniLMv2-L12-H384-mmarco-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-mMiniLMv2-L12-H384-mmarco-mmarcoFR)                                                 | 471MB |        34.22 |    49.20 |       81.70 |       90.90 |       97.10 |        98.90 |
 |  4 | [crossencoder-mpnet-base-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-mpnet-base-mmarcoFR)                                                                               | 438MB |        29.68 |    46.13 |       80.45 |       87.90 |       93.15 |        96.60 |
 |  5 | [crossencoder-distilcamembert-base-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-distilcamembert-base-mmarcoFR)                                                           | 272MB |        27.28 |    43.71 |       80.30 |       89.10 |       95.55 |        98.60 |
 |  6 | [crossencoder-electra-base-french-europeana-cased-discriminator-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-electra-base-french-europeana-cased-discriminator-mmarcoFR) | 443MB |        28.32 |    45.28 |       79.22 |       87.15 |       93.15 |        95.75 |