README.md

metadata

language:
  - multilingual
  - en
  - sw
  - ha
  - yo
  - ig
  - zu
  - sn
  - ar
  - am
  - fr
  - pt
tags:
  - zero-shot-image-classification
  - image generation
  - visual qa
  - text-image embedding
  - image-text embedding
  - pytorch
  - sartify
  - visual conversional ai
  - image semantic retrival
  - african raw resourced languages
  - safetensors
  - clip
license: apache-2.0
library_name: transformers

AViLaMa : African Vision-Languages Aligment Pre-Training Model.

Learning Visual Concepts Directly From African Languages Supervision. Click to see paper

Contents

1. Model Details
2. Uses
3. Training Details
4. Evaluation
5. Acknowledgements
6. Citation
7. How To Get Started With the Model

Model Details

Model Description

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. Inspired from OpenAI CLIP, but with more modalities like video, audio, etc.. and other techniques like agnostic languages encoding, data filtering network. All for more than 12 African languages, trained on the #AViLaDa-2B datasets of filtered image, video, audio-text pairs. We are also working to make it usable in directly vision-vision tasks.

• Developed by : Sartify LLC (www.sartify.com)
• Authors : Innocent Charles, Zephania Reuben
• Funded by : Sartify LLC,Open Source Community, etc..(We always welcome other donors)
• Model type : multilingual & multimodality transformer
• Language(s) : en, sw, ha, yo, ig, zu, sn, ar, am, fr, pt
• License: apache 2.0

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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