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sambert

Sentence Similarity
sentence-transformers
Safetensors
Transformers
Hebrew
bert
feature-extraction
text-embeddings-inference
Inference Endpoints
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Update README.md

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@@ -57,8 +57,8 @@ def mean_pooling(model_output, attention_mask):
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  sentences = ["讗诪讗 讛诇讻讛 诇讙谉", "讗讘讗 讛诇讱 诇讙谉", "讬专拽讜谞讬 拽讜谞讛 诇谞讜 驻讬爪讜转"]
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  # Load model from HuggingFace Hub
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- tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
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- model = AutoModel.from_pretrained('{MODEL_NAME}')
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  # Tokenize sentences
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  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
 
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  sentences = ["讗诪讗 讛诇讻讛 诇讙谉", "讗讘讗 讛诇讱 诇讙谉", "讬专拽讜谞讬 拽讜谞讛 诇谞讜 驻讬爪讜转"]
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  # Load model from HuggingFace Hub
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+ tokenizer = AutoTokenizer.from_pretrained('MPA/sambert')
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+ model = AutoModel.from_pretrained('MPA/sambert')
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  # Tokenize sentences
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  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')