Update README.md

README.md

@@ -1481,7 +1481,7 @@ language:
 
 # [bilingual-embedding-large](https://huggingface.co/Lajavaness/bilingual-embedding-large)
 
-bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large), a pre-trained language model based on the [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large) architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.
+Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large), a pre-trained language model based on the [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large) architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.
 
 
 ## Full Model Architecture
@@ -1501,7 +1501,7 @@ SentenceTransformer(
 - Dataset: [STSB-fr and en]
 - Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library. 
 ### Stage 4: Advanced Augmentation Fine-tuning
-- Dataset: STSB-vn with generate [silver sample from gold sample](https://www.sbert.net/examples/training/data_augmentation/README.html)
+- Dataset: STSB with generate [silver sample from gold sample](https://www.sbert.net/examples/training/data_augmentation/README.html)
 - Method: Employed an advanced strategy using [Augmented SBERT](https://arxiv.org/abs/2010.08240) with Pair Sampling Strategies, integrating both Cross-Encoder and Bi-Encoder models. This stage further refined the embeddings by enriching the training data dynamically, enhancing the model's robustness and accuracy.
 
 
@@ -1517,7 +1517,6 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-from pyvi.ViTokenizer import tokenize
 
 sentences = ["Paris est une capitale de la France", "Paris is a capital of France"]