Sentence Similarity
ANEForge
Safetensors
sentence-transformers
bert
apple-neural-engine
text-embeddings-inference
Instructions to use aneforge/e5-small-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ANEForge
How to use aneforge/e5-small-v2 with ANEForge:
# Run this model's encoder on the Apple Neural Engine, without CoreML. from aneforge.sentence_transformers import SentenceTransformer model = SentenceTransformer("aneforge/e5-small-v2") embeddings = model.encode(["Hello from the Neural Engine"], normalize_embeddings=True) - sentence-transformers
How to use aneforge/e5-small-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aneforge/e5-small-v2") 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] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from aneforge/e5-small-v2: direct link, hf CLI and curl.
- Browser
- Download file 200 Bytes
-
https://huggingface.co/aneforge/e5-small-v2/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://aneforge/e5-small-v2/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/aneforge/e5-small-v2/resolve/main/1_Pooling/config.json
200 Bytes
| { | |
| "word_embedding_dimension": 384, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |