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README.md
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- feature-extraction
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- sentence-similarity
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- transformers
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---
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# mboth/distil-eng-quora-sentence
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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sentences = ['This is an example sentence', 'Each sentence is converted']
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('
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model = AutoModel.from_pretrained('
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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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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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)
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```
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## Citing & Authors
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<!--- Describe where people can find more information -->
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- feature-extraction
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- sentence-similarity
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- transformers
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language:
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- en
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---
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# mboth/distil-eng-quora-sentence
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('gart-labor/eng-distilBERT-se')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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sentences = ['This is an example sentence', 'Each sentence is converted']
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('gart-labor/eng-distilBERT-se')
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model = AutoModel.from_pretrained('gart-labor/eng-distilBERT-se')
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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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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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)
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```
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