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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("llmrails/ember-v1") model = AutoModel.from_pretrained("llmrails/ember-v1", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from llmrails/ember-v1: direct link, hf CLI and curl.
- Browser
- Download file 191 Bytes
-
https://huggingface.co/llmrails/ember-v1/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://llmrails/ember-v1/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/llmrails/ember-v1/resolve/main/1_Pooling/config.json
191 Bytes
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |