Sentence Similarity
sentence-transformers
ONNX
Safetensors
Italian
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:45181
loss:MatryoshkaLoss
loss:CachedMultipleNegativesRankingLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use nickprock/granite-311m-italiano-matryoshka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nickprock/granite-311m-italiano-matryoshka with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nickprock/granite-311m-italiano-matryoshka") sentences = [ "In quali anni ha partecipato Carriera ai Campionati europei?", "Mokgomane è un villaggio del Botswana situato nel distretto Meridionale, sottodistretto di Barolong. Il villaggio, secondo il censimento del 2011, conta 708 abitanti.", "['Carriera\\nCon la ha disputato i Campionati europei del 2001 e i Campionati mondiali del 2002.\\n\\nCon la ha disputato due edizioni dei Campionati europei (2003, 2005).', 'Carriera\\nCon la ha disputato i Campionati mondiali del 2002 e tre edizioni dei Campionati europei (1997, 1999, 2001).\\n\\nCon la ha disputato i Campionati europei del 2003.']", "Carriera Con la ha disputato i Giochi olimpici di Montréal 1976, i Campionati mondiali del 1975 e tre edizioni dei Campionati europei (1974, 1976, 1978)." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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# SentenceTransformer based on ibm-granite/granite-embedding-311m-multilingual-r2
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [ibm-granite/granite-embedding-311m-multilingual-r2](https://huggingface.co/ibm-granite/granite-embedding-311m-multilingual-r2) on the it-wiki-retrieval-synthetic-hn dataset. It maps inputs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.
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<p align="center">
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<img src="https://huggingface.co/nickprock/granite-311m-italiano-matryoshka/raw/main/logo.svg" width="500" alt="Granite Matryoshka Logo">
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# SentenceTransformer based on ibm-granite/granite-embedding-311m-multilingual-r2
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [ibm-granite/granite-embedding-311m-multilingual-r2](https://huggingface.co/ibm-granite/granite-embedding-311m-multilingual-r2) on the [it-wiki-retrieval-synthetic-hn](https://huggingface.co/datasets/nickprock/it-wiki-retrieval-synthetic-hn) dataset. It maps inputs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.
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<p align="center">
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<img src="https://huggingface.co/nickprock/granite-311m-italiano-matryoshka/raw/main/logo.svg" width="500" alt="Granite Matryoshka Logo">
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