Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers
xlm-roberta
feature-extraction
sentence-embedding
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use yco/bilingual-embedding-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yco/bilingual-embedding-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yco/bilingual-embedding-base", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use yco/bilingual-embedding-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("yco/bilingual-embedding-base", trust_remote_code=True) model = AutoModel.from_pretrained("yco/bilingual-embedding-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add new SentenceTransformer model with an onnx backend
Verified signatureHello! *This pull request has been automatically generated from the [`push_to_hub`](https://sbert.net/docs/package_reference/sentence_transformer/SentenceTransformer.html#sentence_transformers.SentenceTransformer.push_to_hub) method from the Sentence Transformers library.* ## Full Model Architecture: ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: ORTModelForFeatureExtraction (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Tip: Consider testing this pull request before merging by loading the model from this PR with the `revision` argument: ```python from sentence_transformers import SentenceTransformer # TODO: Fill in the PR number pr_number =…c1d6484
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initial copy of lajavaness repo
Yoann Couble9dba123