Feature Extraction
sentence-transformers
Safetensors
Transformers
xlm-roberta
sentence-similarity
mteb
text-embeddings-inference
Instructions to use McGill-NLP/AfriE5-Large-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use McGill-NLP/AfriE5-Large-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("McGill-NLP/AfriE5-Large-instruct") 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 McGill-NLP/AfriE5-Large-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="McGill-NLP/AfriE5-Large-instruct")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/AfriE5-Large-instruct") model = AutoModel.from_pretrained("McGill-NLP/AfriE5-Large-instruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 129 Bytes
03904d0 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:380b7a4716a7ffd6db6f9068b3358cb00d0a881e6cf14c9b3753a815ddd4c186
size 6033
|