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")# 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
- Xet hash:
- 128e4bea076b1238bd45d07d4676afd1e6788fe105da8bcc2d0d8cb931b1700d
- Size of remote file:
- 17.1 MB
- SHA256:
- 8373f9cd3d27591e1924426bcc1c8799bc5a9affc4fc857982c5d66668dd1f41
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