edumunozsala
/

bertin-sts-cc-news-es

Sentence Similarity

sentence-transformers

feature-extraction

text-embeddings-inference

Inference Endpoints

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edumunozsala commited on Sep 24, 2022

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a0e27b9

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1 Parent(s): 87bc588

Update README file

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README.md +3 -3

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@@ -29,7 +29,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
-model = SentenceTransformer('bertin-sts-cc-news-es')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -55,8 +55,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('bertin-sts-cc-news-es')
-model = AutoModel.from_pretrained('bertin-sts-cc-news-es')
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
+model = SentenceTransformer('edumunozsala/bertin-sts-cc-news-es')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 sentences = ['This is an example sentence', 'Each sentence is converted']
 # Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('edumunozsala/bertin-sts-cc-news-es')
+model = AutoModel.from_pretrained('edumunozsala/bertin-sts-cc-news-es')
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')