Sentence Similarity
sentence-transformers
Safetensors
Hebrew
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use GiliGold/Knesset-multi-e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use GiliGold/Knesset-multi-e5-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GiliGold/Knesset-multi-e5-large") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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**Knesset-multi-e5-large** is based on the [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) model.
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The transformer encoder has been fine-tuned on [Knesset data](https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus) to better capture legislative and parliamentary language.
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## Usage (Sentence-Transformers)
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Using this model is straightforward if you have [sentence-transformers](https://www.sbert.net) installed:
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**Knesset-multi-e5-large** is based on the [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) model.
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The transformer encoder has been fine-tuned on [Knesset data](https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus) to better capture legislative and parliamentary language.
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[ArXiv](https://arxiv.org/abs/2512.05231)
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## Usage (Sentence-Transformers)
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Using this model is straightforward if you have [sentence-transformers](https://www.sbert.net) installed:
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