A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic
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ImranzamanML
authored
a
paper
1 day ago
ImranzamanML
posted
an
update
9 days ago
Post
2837
🚀 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! 👇
1024m
authored
a
paper
19 days ago
1024m
authored
a
paper
21 days ago
ImranzamanML
posted
an
update
21 days ago
Post
3615
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
-
- 5 replies
1024m
authored
a
paper
26 days ago
ImranzamanML
posted
an
update
29 days ago
Post
1571
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
- 1 reply
Post
1422
🙋🏻♂️Hey there folks,
Did you know that you can use ModernBERT to detect model hallucinations ?
Check out the Demo : Tonic/hallucination-test
See here for Medical Context Demo : MultiTransformer/tonic-discharge-guard
check out the model from KRLabs : KRLabsOrg/lettucedect-large-modernbert-en-v1
and the library they kindly open sourced for it : https://github.com/KRLabsOrg/LettuceDetect
👆🏻if you like this topic please contribute code upstream 🚀
Did you know that you can use ModernBERT to detect model hallucinations ?
Check out the Demo : Tonic/hallucination-test
See here for Medical Context Demo : MultiTransformer/tonic-discharge-guard
check out the model from KRLabs : KRLabsOrg/lettucedect-large-modernbert-en-v1
and the library they kindly open sourced for it : https://github.com/KRLabsOrg/LettuceDetect
👆🏻if you like this topic please contribute code upstream 🚀
- 2 replies
Post
785
Powered by
KRLabsOrg/lettucedect-large-modernbert-en-v1 from KRLabsOrg.
Detect hallucinations in answers based on context and questions using ModernBERT with 8192-token context support!
### Model Details
- **Model Name**: [lettucedect-large-modernbert-en-v1]( KRLabsOrg/lettucedect-large-modernbert-en-v1)
- **Organization**: [KRLabsOrg](
KRLabsOrg
)
- **Github**: [https://github.com/KRLabsOrg/LettuceDetect](https://github.com/KRLabsOrg/LettuceDetect)
- **Architecture**: ModernBERT (Large) with extended context support up to 8192 tokens
- **Task**: Token Classification / Hallucination Detection
- **Training Dataset**: [RagTruth]( wandb/RAGTruth-processed)
- **Language**: English
- **Capabilities**: Detects hallucinated spans in answers, provides confidence scores, and calculates average confidence across detected spans.
LettuceDetect excels at processing long documents to determine if an answer aligns with the provided context, making it a powerful tool for ensuring factual accuracy.
Detect hallucinations in answers based on context and questions using ModernBERT with 8192-token context support!
### Model Details
- **Model Name**: [lettucedect-large-modernbert-en-v1]( KRLabsOrg/lettucedect-large-modernbert-en-v1)
- **Organization**: [KRLabsOrg](
KRLabsOrg
) - **Github**: [https://github.com/KRLabsOrg/LettuceDetect](https://github.com/KRLabsOrg/LettuceDetect)
- **Architecture**: ModernBERT (Large) with extended context support up to 8192 tokens
- **Task**: Token Classification / Hallucination Detection
- **Training Dataset**: [RagTruth]( wandb/RAGTruth-processed)
- **Language**: English
- **Capabilities**: Detects hallucinated spans in answers, provides confidence scores, and calculates average confidence across detected spans.
LettuceDetect excels at processing long documents to determine if an answer aligns with the provided context, making it a powerful tool for ensuring factual accuracy.
ImranzamanML
posted
an
update
3 months ago
Post
3263
Hugging Face just launched the AI Agents Course – a free journey from beginner to expert in AI agents!
- Learn AI Agent fundamentals, use cases and frameworks
- Use top libraries like LangChain & LlamaIndex
- Compete in challenges & earn a certificate
- Hands-on projects & real-world applications
https://huggingface.co/learn/agents-course/unit0/introduction
You can join for a live Q&A on Feb 12 at 5PM CET to learn more about the course here
https://www.youtube.com/live/PopqUt3MGyQ
- Learn AI Agent fundamentals, use cases and frameworks
- Use top libraries like LangChain & LlamaIndex
- Compete in challenges & earn a certificate
- Hands-on projects & real-world applications
https://huggingface.co/learn/agents-course/unit0/introduction
You can join for a live Q&A on Feb 12 at 5PM CET to learn more about the course here
https://www.youtube.com/live/PopqUt3MGyQ
Post
2407
🙋🏻♂️hey there folks ,
Goedel's Theorem Prover is now being demo'ed on huggingface : Tonic/Math
give it a try !
Goedel's Theorem Prover is now being demo'ed on huggingface : Tonic/Math
give it a try !
Post
2993
🙋🏻♂️ Hey there folks ,
our team made a game during the @mistral-game-jam and we're trying to win the community award !
try our game out and drop us a ❤️ like basically to vote for us !
Mistral-AI-Game-Jam/TextToSurvive
hope you like it !
our team made a game during the @mistral-game-jam and we're trying to win the community award !
try our game out and drop us a ❤️ like basically to vote for us !
Mistral-AI-Game-Jam/TextToSurvive
hope you like it !
Post
1918
🙋🏻♂️ Hey there folks ,
Facebook AI just released JASCO models that make music stems .
you can try it out here : Tonic/audiocraft
hope you like it
Facebook AI just released JASCO models that make music stems .
you can try it out here : Tonic/audiocraft
hope you like it
Post
2477
🙋🏻♂️Hey there folks , Open LLM Europe just released Lucie 7B-Instruct model , a billingual instruct model trained on open data ! You can check out my unofficial demo here while we wait for the official inference api from the group :
Tonic/Lucie-7B hope you like it 🚀
valeriaWong
authored
2
papers
4 months ago
ImranzamanML
posted
an
update
5 months ago
Post
711
Deep understanding of (C-index) evaluation measure for better model
Lets start with three patients groups:
Group A
Group B
Group C
For each patient, we will predict risk score (higher score means higher risk of early event).
Step 1: Understanding Concordance Index
The Concordance Index (C-index) evaluate that how well the model ranks survival times.
Understand with sample data:
Group A has 3 patients with actual survival times and predicted risk scores:
Patient Actual Survival Time Predicted Risk Score
P1 5 months 0.8
P2 3 months 0.9
P3 10 months 0.2
Comparable pairs:
(P1, P2): P2 has a shorter survival time and a higher risk score → Concordant ✅
(P1, P3): P3 has a longer survival time and a lower risk score → Concordant ✅
(P2, P3): P3 has a longer survival time and a lower risk score → Concordant ✅
Total pairs = 3
Total concordant pairs = 3
C-index for Group A = Concordant pairs/Total pairs= 3/3 = 1.0
Step 2: Calculate C-index for All Groups
Repeat the process for all groups. For now we can assume:
Group A: C-index = 1.0
Group B: C-index = 0.8
Group C: C-index = 0.6
Step 3: Stratified Concordance Index
The Stratified Concordance Index combines the C-index scores of all groups and focusing on the following:
Average performance across groups (mean of C-indices).
Consistency across groups (low standard deviation of C-indices).
Formula:
Stratified C-index = Mean(C-index scores) - Standard Deviation(C-index scores)
Calculate the mean:
Mean=1.0 + 0.8 + 0.6/3 = 0.8
Calculate the standard deviation:
Standard Deviation= sqrt((1.0-0.8)^2 + (0.8-0.8)^2 + (0.6-0.8)^/3) = 0.16
Stratified C-index:
Stratified C-index = 0.8 - 0.16 = 0.64
Step 4: Interpret the Results
A high Stratified C-index means:
The model predicts well overall (high mean C-index).
Lets start with three patients groups:
Group A
Group B
Group C
For each patient, we will predict risk score (higher score means higher risk of early event).
Step 1: Understanding Concordance Index
The Concordance Index (C-index) evaluate that how well the model ranks survival times.
Understand with sample data:
Group A has 3 patients with actual survival times and predicted risk scores:
Patient Actual Survival Time Predicted Risk Score
P1 5 months 0.8
P2 3 months 0.9
P3 10 months 0.2
Comparable pairs:
(P1, P2): P2 has a shorter survival time and a higher risk score → Concordant ✅
(P1, P3): P3 has a longer survival time and a lower risk score → Concordant ✅
(P2, P3): P3 has a longer survival time and a lower risk score → Concordant ✅
Total pairs = 3
Total concordant pairs = 3
C-index for Group A = Concordant pairs/Total pairs= 3/3 = 1.0
Step 2: Calculate C-index for All Groups
Repeat the process for all groups. For now we can assume:
Group A: C-index = 1.0
Group B: C-index = 0.8
Group C: C-index = 0.6
Step 3: Stratified Concordance Index
The Stratified Concordance Index combines the C-index scores of all groups and focusing on the following:
Average performance across groups (mean of C-indices).
Consistency across groups (low standard deviation of C-indices).
Formula:
Stratified C-index = Mean(C-index scores) - Standard Deviation(C-index scores)
Calculate the mean:
Mean=1.0 + 0.8 + 0.6/3 = 0.8
Calculate the standard deviation:
Standard Deviation= sqrt((1.0-0.8)^2 + (0.8-0.8)^2 + (0.6-0.8)^/3) = 0.16
Stratified C-index:
Stratified C-index = 0.8 - 0.16 = 0.64
Step 4: Interpret the Results
A high Stratified C-index means:
The model predicts well overall (high mean C-index).
Post
3609
🙋🏻♂️hey there folks,
periodic reminder : if you are experiencing ⚠️500 errors ⚠️ or ⚠️ abnormal
we have a thread 👉🏻 https://discord.com/channels/879548962464493619/1295847667515129877
if you can record the problem and share it there , or on the forums in your own post , please dont be shy because i'm not sure but i do think it helps 🤗🤗🤗
periodic reminder : if you are experiencing ⚠️500 errors ⚠️ or ⚠️ abnormal
spaces behavior on load or launch ⚠️we have a thread 👉🏻 https://discord.com/channels/879548962464493619/1295847667515129877
if you can record the problem and share it there , or on the forums in your own post , please dont be shy because i'm not sure but i do think it helps 🤗🤗🤗
- 2 replies
Alignment-Lab-AI
posted
an
update
6 months ago
Post
1426
remember boys and girls, always keep all your data, its never a waste of time!