Update README.md
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README.md
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@@ -32,7 +32,7 @@ Then you can use the model like this:
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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('paecter')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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@@ -58,8 +58,8 @@ def mean_pooling(model_output, attention_mask):
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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('paecter')
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model = AutoModel.from_pretrained('paecter')
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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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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('mpi-inno-comp/paecter')
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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('mpi-inno-comp/paecter')
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model = AutoModel.from_pretrained('mpi-inno-comp/paecter')
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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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