Arabert-EOU-detection-model

Arabic End-of-Turn (EOU) Detection Model — AraBERT Fine-Tuned

This model fine-tunes AraBERT for detecting end-of-turn (EOU) boundaries in Arabic dialogue.
It predicts whether a given user message represents a continuation or an end of turn.

• Repository: nihad-ask/Arabert-EOU-detection-model
• Task: Binary End-of-Utterance Classification
• Language: Arabic (MSA + Dialects)
• Base Model: aubmindlab/bert-base-arabertv2

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🚦 Task Definition

This is a binary classification task:

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📌 Use Cases

• Conversational AI / Chatbots
• Dialogue Systems
• Turn-taking prediction
• Speech-to-text segmentation
• Customer support automation

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📊 Evaluation

Balanced Validation Set

Accuracy: 0.9539

Overall:

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Test Set

Accuracy: 0.8919

Overall:

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from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "nihad-ask/Arabert-EOU-detection-model"

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

text = "تمام و بعدين؟"

inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits, dim=1).item()

if prediction == 1:
    print("End of turn")
else:
    print("Speaker will continue")