sartifyllc
/

AViLaMa

@@ -32,7 +32,6 @@ 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.
@@ -52,36 +51,25 @@ 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
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
 ## Bias, Risks, and Limitations
@@ -95,133 +83,4 @@ model = model.eval()
 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
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### 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]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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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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 # 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.
 tokenizer = AutoTokenizer.from_pretrained("sartifyllc/AViLaMa")
 model = model.eval()
 ```
+## Model Sources
+- **Repository :** [AViLaMa-Sources](https://github.com/Sartify/AViLaMa-Sources)
+- **Datasets :** Coming...
+- **Paper :** Coming...
+- **Demo :** Coming...
+### Direct & Downstream Use:
+1. zero shot semantic image retrieval and ranking tasks.
+2. zero shot semantic audio retrieval and ranking tasks.
+3. zero shot semantic video retrieval and ranking tasks.
+4. zero shot image classification tasks.
+5. Zero shot video classification tasks.
+6. Zero shot audio classification tasks.
+7. visual QA tasks.
+8. visual conversional GenAI tasks.
+9. image and video capturing tasks.
+10. images and art generation tasks.
+11. text-images analysis tasks.
+12. content moderation task.
 ## Bias, Risks, and Limitations
 Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.