AMR-KELEG

[](https://github.com/AMR-KELEG/ALDi) [](https://huggingface.co/spaces/AMR-KELEG/ALDi) A BERT-based model fine-tuned to estimate the Arabic Level of Dialectness of text. Model | Link on 🤗 ---|--- Sentence-ALDi (random seed: 42) | https://huggingface.co/AMR-KELEG/Sentence-ALDi Sentence-ALDi (random seed: 30) | https://huggingface.co/AMR-KELEG/Sentence-ALDi-30 Sentence-ALDi (random seed: 50) | https://huggingface.co/AMR-KELEG/Sentence-ALDi-50 Token-DI (random seed: 42) | https://huggingface.co/AMR-KELEG/ALDi-Token-DI Token-DI (random seed: 30) | https://huggingface.co/AMR-KELEG/ALDi-Token-DI-30 Token-DI (random seed: 50) | https://huggingface.co/AMR-KELEG/ALDi-Token-DI-50 - Model type: Regression head on top of a BERT-based model fine-tuned for estimating the Arabic Level of Dialectness of text. - Language(s) (NLP): Arabic. - Finetuned from model: MarBERT - Dataset: AOC-ALDi If you find the model useful, please cite the following respective paper:

A BERT-based model fine-tuned to perform single-label Arabic Dialect Identification (ADI). Instead of predicting the most probable dialect, the logits are used to generate multilabel predictions. - Model type: A Dialect Identification model fine-tuned on the training sets of: NADI2020,2021,2023 and MADAR 2018. - Language(s) (NLP): Arabic. Multilabel country-level Dialect Identification Baseline I (Top 90%) If you find the model useful, please cite the following respective paper: