SeyedAli
/

Multilingual-Text-Semantic-Search-Siamese-BERT-V1

Sentence Similarity

sentence-transformers

feature-extraction

text-embeddings-inference

Inference Endpoints

Model card Files Files and versions Community

SeyedAli commited on Sep 26, 2023

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1 Parent(s): a2bb023

Update README.md

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@@ -97,8 +97,8 @@ query = "How many people live in London?"
 docs = ["Around 9 Million people live in London", "London is known for its financial district"]
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
-model = AutoModel.from_pretrained("sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
 #Encode query and docs
 query_emb = encode(query)
@@ -154,8 +154,8 @@ query = "How many people live in London?"
 docs = ["Around 9 Million people live in London", "London is known for its financial district"]
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
-model = TFAutoModel.from_pretrained("sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
 #Encode query and docs
 query_emb = encode(query)

 docs = ["Around 9 Million people live in London", "London is known for its financial district"]
 # Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained("SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1")
+model = AutoModel.from_pretrained("SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1")
 #Encode query and docs
 query_emb = encode(query)
 docs = ["Around 9 Million people live in London", "London is known for its financial district"]
 # Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained("SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1")
+model = TFAutoModel.from_pretrained("SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1")
 #Encode query and docs
 query_emb = encode(query)