Feature Extraction
sentence-transformers
PyTorch
Safetensors
Transformers
English
bert
mteb
sentence-similarity
Eval Results (legacy)
text-embeddings-inference
Instructions to use llmrails/ember-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use llmrails/ember-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("llmrails/ember-v1") 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 llmrails/ember-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="llmrails/ember-v1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("llmrails/ember-v1") model = AutoModel.from_pretrained("llmrails/ember-v1") - Inference
- Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by olivierdehaene - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:e518be275958e79b6b97206171cfa6904418fc25e3afcba96eb9a980e624bb67
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size 1340612432
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