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Muhammad Imran Zaman PRO
ImranzamanML
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AI & ML interests
Results-driven Machine Learning Engineer with 7+ years of experience leading teams and delivering advanced AI solutions that increased revenue by up to 40%. Proven track record in enhancing business performance through consultancy and expertise in NLP, Computer Vision, LLM models and end-to-end ML pipelines. Skilled in managing critical situations and collaborating with cross-functional teams to implement scalable, impactful solutions. Kaggle Grandmaster and top performer in global competitions, dedicated to staying at the forefront of AI advancements.
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ImranzamanML/Llama3.1-1B-finetuned-medical-expert
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ImranzamanML/Llama3.1-1B-finetuned-medical-expert
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ImranzamanML's activity
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🚀 New paper out: "Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function"
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function (2410.03979)
In this work, we tackle some major challenges in Arabic multi-label emotion classification especially the issues of class imbalance and label correlation that often hurt model performance, particularly for minority emotions.
Our approach:
Stacked contextual embeddings from fine-tuned ArabicBERT, MarBERT, and AraBERT models.
A meta-learning strategy that builds richer representations.
A hybrid loss function combining class weighting, label correlation matrices, and contrastive learning to better handle class imbalances.
🧠 Model pipeline: stacked embeddings → meta-learner → Bi-LSTM → fully connected network → multi-label classification.
🔍 Extensive experiments show significant improvements across Precision, Recall, F1-Score, Jaccard Accuracy, and Hamming Loss.
🌟 The hybrid loss function in particular helped close the gap between majority and minority classes!
We also performed ablation studies to break down each component’s contribution and the results consistently validated our design choices.
This framework isn't just for Arabic it offers a generalizable path for improving multi-label emotion classification in other low-resource languages and domains.
Big thanks to my co-authors: Muhammad Azeem Aslam, Wang Jun, Nisar Ahmed, Li Yanan, Hu Hongfei, Wang Shiyu, and Xin Liu!
Would love to hear your thoughts on this work! 👇
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function (2410.03979)
In this work, we tackle some major challenges in Arabic multi-label emotion classification especially the issues of class imbalance and label correlation that often hurt model performance, particularly for minority emotions.
Our approach:
Stacked contextual embeddings from fine-tuned ArabicBERT, MarBERT, and AraBERT models.
A meta-learning strategy that builds richer representations.
A hybrid loss function combining class weighting, label correlation matrices, and contrastive learning to better handle class imbalances.
🧠 Model pipeline: stacked embeddings → meta-learner → Bi-LSTM → fully connected network → multi-label classification.
🔍 Extensive experiments show significant improvements across Precision, Recall, F1-Score, Jaccard Accuracy, and Hamming Loss.
🌟 The hybrid loss function in particular helped close the gap between majority and minority classes!
We also performed ablation studies to break down each component’s contribution and the results consistently validated our design choices.
This framework isn't just for Arabic it offers a generalizable path for improving multi-label emotion classification in other low-resource languages and domains.
Big thanks to my co-authors: Muhammad Azeem Aslam, Wang Jun, Nisar Ahmed, Li Yanan, Hu Hongfei, Wang Shiyu, and Xin Liu!
Would love to hear your thoughts on this work! 👇
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6 days ago
commented on
Fine-Tuning 1B LLaMA 3.2: A Comprehensive Step-by-Step Guide with Code
7 days ago
Hi, you can remove dataset_text_field="text" or can update "pip install -q trl==0.12.0"
I finetuned this model before and at that time dataset_text_field was required by unsloth. Thanks
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13 days ago
replied to
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13 days ago
I used this data https://huggingface.co./datasets/Amod/mental_health_counseling_conversations/tree/main and also you can find here as well https://www.kaggle.com/datasets/melissamonfared/mental-health-counseling-conversations-k
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14 days ago
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3798
We’re back—with higher stakes, new datasets, and more chances to stand out. Duality AI's Synthetic-to-Real Object Detection Challenge 2 is LIVE!🚦
✍ Sign up here: https://lnkd.in/g2avFP_X
After the overwhelming response to Challenge 1, we're pushing the boundaries even further in Challenge 2, where your object detection models will be put to the test in the real world after training only on synthetic data.
👉 Join our Synthetic-to-Real Object Detection Challenge 2 on Kaggle!
What’s Different This Time? Unlike our first challenge, we’re now diving deep into data manipulation. Competitors can:
🔹Access 4 new supplemental datasets via FalconCloud with varying lighting, occlusions, and camera angles.
🔹Generate your own synthetic datasets using FalconEditor to simulate edge cases.
🔹Mix, match, and build custom training pipelines for maximum mAP@50 performance
This challenge isn’t just about using synthetic data—it’s about mastering how to craft the right synthetic data.
Ready to test your skills?
🏆The Challenge
Train an object detection model using synthetic images created with Falcon—Duality AI's cutting-edge digital twin simulation software—then evaluate your model on real-world imagery.
The Twist?
📈Boost your model’s accuracy by creating and refining your own custom synthetic datasets using Falcon!
Win Cash Prizes & Recognition
🔹Earn cash and public shout-outs from the Duality AI accounts
Enhance Your Portfolio
🔹Demonstrate your real-world AI and ML expertise in object detection to prospective employers and collaborators.
🔹Expand Your Network
🔹Engage, compete, and collaborate with fellow ML engineers, researchers, and students.
🚀 Put your skills to the test and join our Kaggle competition today: https://lnkd.in/g2avFP_X
✍ Sign up here: https://lnkd.in/g2avFP_X
After the overwhelming response to Challenge 1, we're pushing the boundaries even further in Challenge 2, where your object detection models will be put to the test in the real world after training only on synthetic data.
👉 Join our Synthetic-to-Real Object Detection Challenge 2 on Kaggle!
What’s Different This Time? Unlike our first challenge, we’re now diving deep into data manipulation. Competitors can:
🔹Access 4 new supplemental datasets via FalconCloud with varying lighting, occlusions, and camera angles.
🔹Generate your own synthetic datasets using FalconEditor to simulate edge cases.
🔹Mix, match, and build custom training pipelines for maximum mAP@50 performance
This challenge isn’t just about using synthetic data—it’s about mastering how to craft the right synthetic data.
Ready to test your skills?
🏆The Challenge
Train an object detection model using synthetic images created with Falcon—Duality AI's cutting-edge digital twin simulation software—then evaluate your model on real-world imagery.
The Twist?
📈Boost your model’s accuracy by creating and refining your own custom synthetic datasets using Falcon!
Win Cash Prizes & Recognition
🔹Earn cash and public shout-outs from the Duality AI accounts
Enhance Your Portfolio
🔹Demonstrate your real-world AI and ML expertise in object detection to prospective employers and collaborators.
🔹Expand Your Network
🔹Engage, compete, and collaborate with fellow ML engineers, researchers, and students.
🚀 Put your skills to the test and join our Kaggle competition today: https://lnkd.in/g2avFP_X
posted
an
update
14 days ago
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3602
Mental Health Chatbot by Fine-Tuning Llama 4
https://huggingface.co./blog/ImranzamanML/llama-4-fine-tuning-with-mental-health-counseling
https://huggingface.co./blog/ImranzamanML/llama-4-fine-tuning-with-mental-health-counseling
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- 4 replies
published
an
article
14 days ago
Article
LLaMA 4 Fine-Tuning with Mental Health Counseling Data
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22 days ago
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update
22 days ago
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1569
Llama 4 is here and it's making serious waves!
After diving into the latest benchmark results, it’s clear that Meta’s new Llama 4 lineup (Maverick, Scout, and Behemoth) is no joke.
Here are a few standout highlights🔍:
Llama 4 Maverick hits the sweet spot between cost and performance
- Outperforms GPT-4o in image tasks like ChartQA (90.0 vs 85.7) and DocVQA (94.4 vs 92.8)
- Beats others in MathVista and MMLU Pro too and at a fraction of the cost ($0.19–$0.49 vs $4.38 🤯)
Llama 4 Scout is lean, cost-efficient, and surprisingly capable
- Strong performance across image and language tasks (e.g. ChartQA: 88.8, DocVQA: 94.4)
- More affordable than most competitors and still beats out larger models like Gemini 2.0 Flash-Lite
Llama 4 Behemoth is the heavy hitter.
- Tops the charts in LiveCodeBench (49.4), MATH-500 (95.0), and MMLU Pro (82.2)
- Even edges out Claude 3 Sonnet and Gemini 2 Pro in multiple areas
Meta didn’t just show up, they delivered across multimodal, coding, reasoning, and multilingual benchmarks.
And honestly? Seeing this level of performance, especially at lower inference costs, is a big deal for anyone building on LLMs.
Curious to see how these models do in real-world apps next.
#AI #Meta #Llama4 #LLMs #Benchmarking #MachineLearning #OpenSourceAI #GenerativeAI
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