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README.md
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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---
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# indoSBERT-large
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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IndoSBERT is a modification of `https://huggingface.co/indobenchmark/indobert-large-p1` that has been fine-tuned using the siamese network scheme inspired by SBERT.
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This model was fine-tuned with the STS Dataset (2012-2016) which was machine-translated into Indonesian languange.
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This model can provide meaningful semantic sentence embeddings for Indonesian sentences.
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<!--- Describe your model here -->
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```python
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from sentence_transformers import SentenceTransformer
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sentences = ["
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model = SentenceTransformer('denaya/indoSBERT-large')
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embeddings = model.encode(sentences)
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## Evaluation Results
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<!--- Describe how your model was evaluated -->
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=indobert-large-p1_dense_trained-epoch14)
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## Training
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The model was trained with the parameters:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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language:
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- id
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---
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# indoSBERT-large
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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IndoSBERT is a modification of `https://huggingface.co/indobenchmark/indobert-large-p1` that has been fine-tuned using the siamese network scheme inspired by SBERT.
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This model was fine-tuned with the STS Dataset (2012-2016) which was machine-translated into Indonesian languange.
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This model can provide meaningful semantic sentence embeddings for Indonesian sentences.
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<!--- Describe your model here -->
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```python
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from sentence_transformers import SentenceTransformer
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sentences = ["Komposer favorit saya adalah Joe Hisaishi", "Sapo tahu enak banget"]
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model = SentenceTransformer('denaya/indoSBERT-large')
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embeddings = model.encode(sentences)
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## Training
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The model was trained with the parameters:
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