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
Excellent model
#2
by xddtc48jo - opened
Hello,
You've made a very good model. Very happy using it in my personal search engine.
Any chance for the v2 of this model in the next trimester?
Thank you!
Hi @xddtc48jo ,
Thank you!
Yes, we are planning to release the v2 model in Feb.
I would love to hear your feedback/suggestions or use cases.
w95 changed discussion status to closed