firqaaa
/

indo-sentence-bert-base

Sentence Similarity

sentence-transformers

feature-extraction

text-embeddings-inference

Inference Endpoints

Model card Files Files and versions Community

Firqa Aqila Noor A commited on Sep 19, 2022

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1 Parent(s): 7205897

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@@ -27,7 +27,8 @@ Then you can use the model like this:
 ```python
 from sentence_transformers import SentenceTransformer
 sentences = ["Ibukota Perancis adalah Paris", "Menara Eifel terletak di Paris, Perancis",
-            "Pizza adalah makanan khas Italia", "Saya kuliah di Carneige Melon University"]
 model = SentenceTransformer('firqaaa/indo-sbert-finetuned-snli-mnli-id')
 embeddings = model.encode(sentences)
@@ -52,8 +53,10 @@ def mean_pooling(model_output, attention_mask):
 # Sentences we want sentence embeddings for
-sentences = ["Ibukota Perancis adalah Paris", "Menara Eifel terletak di Paris, Perancis",
-            "Pizza adalah makanan khas Italia", "Saya kuliah di Carneige Melon University"]
 # Load model from HuggingFace Hub

 ```python
 from sentence_transformers import SentenceTransformer
 sentences = ["Ibukota Perancis adalah Paris", "Menara Eifel terletak di Paris, Perancis",
+            "Pizza adalah makanan khas Italia",
+            "Saya kuliah di Carneige Melon University"]
 model = SentenceTransformer('firqaaa/indo-sbert-finetuned-snli-mnli-id')
 embeddings = model.encode(sentences)
 # Sentences we want sentence embeddings for
+sentences = ["Ibukota Perancis adalah Paris",
+             "Menara Eifel terletak di Paris, Perancis",
+             "Pizza adalah makanan khas Italia",
+             "Saya kuliah di Carneige Melon University"]
 # Load model from HuggingFace Hub