Arabertv02-classifier-FA

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FA-AraBert is an Arabic binary text classification model designed to detect whether a user query is related to first-aid. The classifier serves as the intent detection and safety filtering component of an MSA first-aid chatbot pipeline. Two models are developed and evaluated in this project: FA-AraBERTv2 and FA-AraBERTv0.2, both fine-tuned on the FALAH-Mix dataset (1,028 question–answer (QA) pairs, including 924 non first-aid pairs and 104 first-aid pairs) based on AraBERT base models. These classifiers were systematically compared under multiple training configurations to identify the most suitable model for deployment.

Model Details

Model Description

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• Model name: FA-AraBERTv0.2
• Developed by: MABROUK Imane
• Supervised by: Dr. Rana R. Malhas (bigIR Research Group, Qatar University) & Dr. Imane Chlioui (INSEA)
• Task: Binary text classification
• Domain: First Aid / Emergency Care
• Labels:

<p align="center"> <img src="./labels.png" width="300"/> </p> - **Funded by:** Self-funded academic project (Academic graduation project)

The FA-AraBERT classifier was developed as part of the PFE project titled:

• Towards Building an Arabic First-Aid Chatbot using FA-AraBERT Classifier and FALAH Dataset.

• Shared by: MABROUK Imane

• Model type: Transformer-based text classification model (BERT architecture)

• Language(s) (NLP): MSA(Modern Standard Arabic)

• License: Apache 2.0

• Finetuned from model: AraBERTv02

Model Sources

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• Repository: https://github.com/Imymab/FA-AraBERT-Classifier

Uses

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Direct Use

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This model can be used directly for:

• First-aid and emergency query detection
• Binary text classification (First-aid/Non First-aid)

Downstream Use

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The model can be integrated into:

• Medical conversational agents
• Healthcare assistance tools for emergency guidance

Out-of-Scope Use

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This model is not suitable for:

• Use in high-stakes clinical decision-making without human supervision
• Non-Arabic text processing
• Tasks requiring deep medical reasoning or long-context understanding

Bias, Risks, and Limitations

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• The model is trained on a limited dataset (FALAH-Mix), which may not fully represent all medical domains.
• Class imbalance in the dataset may affect performance on underrepresented categories.
• The model may reflect biases present in the existing arabic medical datasets (AHD, MAQA).
• It is not a substitute for professional medical advice.

Recommendations

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• Try to explore additional techniques to improve performance, particularly by addressing the class imbalance in the FALAH-Mix dataset (10% first-aid, 90% non–first-aid).

How to Get Started with the Model

Use the code below to get started with the model:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "imaneumabderahmane/Arabertv02-classifier-FA"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "ما هي الإسعافات الأولية لحروق الدرجة الأولى؟"
inputs = tokenizer(text, return_tensors="pt", truncation=True)

with torch.no_grad():
    outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=-1).item()

print(prediction)

Training Details

Training Data

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The FA-AraBERTv02 classifier was trained and evaluated on the FALAH-Mix dataset, which contains 1,028 Arabic question–answer pairs (924 non–first-aid QA pairs and 104 first-aid QA pairs). The dataset exhibits a strong class imbalance, with approximately 90% non–first-aid queries and 10% first-aid queries. The data was split into training, development, and test sets while preserving the original class distribution.

For more details about the FALAH and FALAH-Mix datasets, developed as part of the PFE project, please refer to: https://huggingface.co/datasets/imaneumabderahmane/FALAH

To mitigate class imbalance, the training set was augmented with additional first-aid samples from external datasets, including the Mayo Clinic First-Aid dataset (374 first-aid QA pairs) and the 68 first-aid QA pairs from the AHD dataset. This resulted in a balanced training set of 1,184 samples, while the development and test sets remained unchanged.

The following table presents the FALAH-Mix dataset before balancing the training set:

<p align="center"> <img src="./distribution of the FALAH-Mix dataset.png" width="900"/> </p>

The following table presents the FALAH-Mix dataset after balancing the training set:

<p align="center"> <img src="./table FALAH-Mix training set balanced.png" width="700"/> </p>

Note: The FALAH-Mix dataset was split according to the emergency labels. As a result, the training, development, and test sets each contain approximately 90% non–first-aid QA pairs and 10% first-aid QA pairs. The balancing strategy was applied only to the training set in order to evaluate its impact on the classifier.

Training Procedure

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Preprocessing

• Arabic text normalization
• Translating questions from Arabic Dialect into Modern Standard Arabic using this model: https://huggingface.co/HamzaNaser/Dialects-to-MSA-Transformer
• Tokenization using AraBERT tokenizer

Training Hyperparameters

The models were fine-tuned using supervised learning with the following configuration:

• Optimizer: AdamW
• Learning rate: 3 × 10⁻⁵
• Batch size: 16
• Epochs: 3
• Loss function: Cross-entropy
• Mixed-precision training enabled <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times

Evaluation

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Testing Data, Factors & Metrics

Testing Data

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The test split from the FALAH-Mix dataset: https://huggingface.co/datasets/imaneumabderahmane/FALAH

Metrics

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• Macro F1-score, chosen to account for class imbalance

Results

The following Table summarizes the Macro F1 scores obtained by FA-AraBERTv2 and FA-AraBERTv0.2 under different training configurations.

<p align="center"> <img src="./table results classifier arabert.png" width="800"/> </p> The best performance was achieved when fine-tuning on the balanced FALAH-Mix training set with class weighting. FA-AraBERTv2 achieved a Macro F1-score of 0.6379, slightly outperforming FA-AraBERTv0.2. Due to this consistent advantage, FA-AraBERTv2 was selected for deployment in the final chatbot system. It should be noted that statistical significance testing was not performed due to time constraints related to the project.