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ounachad/
araelectra-mlm-dapt-ahqad

Name: araelectra-mlm-dapt-ahqad
Availability: InStock

safetensors

arabic

medical

masked-language-modeling

dapt

araelectra

apache-2.0

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AraELECTRA-Generator-MLM (DAPT on AHQAD/AHD)

This repository releases a domain-adapted AraELECTRA generator checkpoint continued with masked language modeling (DAPT-MLM) on the AHQAD/AHD Arabic health question–answer corpus.
The model is intended for constrained question reformulation via mask filling (e.g., replacing a placeholder with one or more [MASK] tokens and predicting only the masked positions).

Model ID

USERNAME/REPO_NAME

Training data

AHQAD/AHD Arabic health QA corpus (≈808k Q–A pairs, ~90 specialties).
Used under the original terms of the dataset.

Intended use

Arabic clinical question rewriting/reformulation using span completion (mask filling).
A front-end module for Arabic clinical QA pipelines (retrieval/generation) to improve question clarity and completeness.

How to use (Transformers)

python

from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch

repo_id = "USERNAME/REPO_NAME"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForMaskedLM.from_pretrained(repo_id)

text = "عندي ألم في ___ منذ أسبوع؟"
# example: replace the placeholder with one or more [MASK]
masked = text.replace("___", tokenizer.mask_token)

inputs = tokenizer(masked, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits

# get the top prediction for the first mask position
mask_index = (inputs["input_ids"][0] == tokenizer.mask_token_id).nonzero(as_tuple=True)[0][0].item()
pred_id = logits[0, mask_index].argmax(-1).item()
print("Prediction:", tokenizer.decode([pred_id]))

ounachad/araelectra-mlm-dapt-ahqad