Feature Extraction
sentence-transformers
ONNX
Safetensors
Transformers
xlm-roberta
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Nextcloud-AI/multilingual-e5-large-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Nextcloud-AI/multilingual-e5-large-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Nextcloud-AI/multilingual-e5-large-instruct") 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] - Transformers
How to use Nextcloud-AI/multilingual-e5-large-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Nextcloud-AI/multilingual-e5-large-instruct")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Nextcloud-AI/multilingual-e5-large-instruct") model = AutoModel.from_pretrained("Nextcloud-AI/multilingual-e5-large-instruct") - Notebooks
- Google Colab
- Kaggle
Commit ·
2696861
1
Parent(s): b9fbfc1
Add link to new paper (#1)
Browse files- Add link to new paper (040d0121a88973cae67c013d62b14ef9a65ee4ec)
Co-authored-by: Omar Sanseviero <osanseviero@users.noreply.huggingface.co>
README.md
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[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
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Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
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This model has 24 layers and the embedding size is 1024.
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## Usage
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[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
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Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
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[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/abs/2402.05672).
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Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
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This model has 24 layers and the embedding size is 1024.
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## Usage
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