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.
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2from transformers import AutoTokenizer, FlaxVisionTextDualEncoderModel
3model = FlaxVisionTextDualEncoderModel.from_pretrained("LinaAlhuri/Arabic-clip-vit-base-patch32", logit_scale_init_value=1,from_pt=True)
4model.save_pretrained("arabic_clip")
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6tokenizer = AutoTokenizer.from_pretrained("asafaya/bert-base-arabic", cache_dir=None, use_fast=True)
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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.
We have tested the efficacy of Arabic CLIP across different benchmarks tailored for tasks like zero-shot learning, image retrieval, localization, and image search.
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.
