Feature Extraction
sentence-transformers
Safetensors
Transformers
mteb
custom_code
Eval Results (legacy)
Instructions to use jxm/cde-small-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jxm/cde-small-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jxm/cde-small-v1", trust_remote_code=True) 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 jxm/cde-small-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jxm/cde-small-v1", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jxm/cde-small-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Tom Aarsen commited on
Commit ·
9677008
1
Parent(s): aea6a04
Replace local-only "." with "jxm/cde-small-v1"
Browse files
README.md
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@@ -8730,7 +8730,7 @@ from sentence_transformers import SentenceTransformer
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from datasets import load_dataset
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# 1. Load the Sentence Transformer model
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model = SentenceTransformer("
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context_docs_size = model[0].config.transductive_corpus_size # 512
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# 2. Load the dataset: context dataset, docs, and queries
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from datasets import load_dataset
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# 1. Load the Sentence Transformer model
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model = SentenceTransformer("jxm/cde-small-v1", trust_remote_code=True)
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context_docs_size = model[0].config.transductive_corpus_size # 512
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# 2. Load the dataset: context dataset, docs, and queries
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