sartifyllc/AViLaData
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How to use sartifyllc/AViLaMa with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("zero-shot-image-classification", model="sartifyllc/AViLaMa")
pipe(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png",
candidate_labels=["animals", "humans", "landscape"],
) # Load model directly
from transformers import AutoProcessor, AutoModel
processor = AutoProcessor.from_pretrained("sartifyllc/AViLaMa")
model = AutoModel.from_pretrained("sartifyllc/AViLaMa")Learning Visual Concepts Directly From African Languages Supervision. Paper is coming
AViLaMa is the large open-source text-vision alignment pre-training model in African languages. It brings a way to learn visual concepts directly from African languages supervision. Based on African languages to capture the nuances, cultural context, and social aspect use of our languages that are so impossible to get just from machine translation. It includes techniques like agnostic languages encoding, data filtering network etc... All for more than 12 African languages, trained on the #AViLaDa-2B datasets of filtered image-text pairs.
import torch
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("sartifyllc/AViLaMa")
tokenizer = AutoTokenizer.from_pretrained("sartifyllc/AViLaMa")
model = model.eval()
BibTeX:
AViLaMa paper
@article{sartifyllc2023africanvision,
title={AViLaMa: Learning Visual Concepts Directly From African Languages Supervision},
author={Sartify LLC Research Team},
journal={To be inserted},
year={2024}
}