LinaAlhuri

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Arabic-clip-vit-base-patch16

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Model Details

Arabic CLIP is an adaptation of the Contrastive Language-Image Pre-training (CLIP) for the Arabic language. CLIP is an OpenAI-developed model that learns conceptual concepts from images and relates them with textual descriptions. This work attempts to improve the model's understanding and interpretation of visual information in the context of the Arabic language.

Model Use

from transformers import AutoTokenizer, FlaxVisionTextDualEncoderModel
model = FlaxVisionTextDualEncoderModel.from_pretrained("LinaAlhuri/Arabic-clip-vit-base-patch16", logit_scale_init_value=1,from_pt=True)
model.save_pretrained("arabic_clip") 

tokenizer = AutoTokenizer.from_pretrained("asafaya/bert-base-arabic", cache_dir=None, use_fast=True)

Data

The aim was to create a comprehensive Arabic image-text dataset by combining various data sources due to the scarcity of Arabic resources. Challenges included limited Arabic data and the quality of translated datasets. The approach involved merging genuine datasets for rich information and using translated datasets to cover diverse domains, scenarios, and objects, striking a balance between their respective pros and cons.

Performance and Limitations

We have tested the efficacy of Arabic CLIP across different benchmarks tailored for tasks like zero-shot learning, image retrieval, localization, and image search.

• Conceptual Captions
• COCO
• ImageNet
• Unsplash

Limitations

To summarize the limitations into points

• Arabic CLIP struggles to count after 3.
• Limited genuine samples for the Arabic language.
• Various noises and biases might be introduced into Arabic CLIP because no studies have been conducted yet to address this issue in the published Arabic dataset or Arabic language models.

Bias

For gender bias, it is important to note that Arabic uses a two-gender system in which all nouns are classified as masculine or feminine. However, this is not the case for English. Translating the text from English to Arabic may result in information loss or even make it prone to gender bias.