README.md

---
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](www.sartify.com)


## Model Details
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 (English), sw (Swahili), ha (Hausa), yo (Yoruba), ig (Igbo), zu (Zulu), sn (Shona), ar (Arabic), am (Amharic), fr (French), pt (Portuguese)
- **License:** apache 2.0

## Load model from hugging face. 
```python
import torch
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("sartifyllc/AViLaMa")
tokenizer = AutoTokenizer.from_pretrained("sartifyllc/AViLaMa")
model = model.eval()
```

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository :** (AViLaMa-Sources)[https://github.com/Sartify/AViLaMa-Sources]
- **Paper :** Comming...
- **Demo :** Comming...

## Uses

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### 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

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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

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### 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

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## 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]

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**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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