Hate Speech Detection Code

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@amirrmusavifr

Hi, I'm an experienced Python developer with the necessary skills to complete your project. I already worked on NLP project topics including skill sets: • Proficiency in Machine Learning techniques, deep networks (CNN, RNN, LSTM, GRU, Attention Mechanism), • Experience with Chatbot, Question Answering, Sentiment Classification, Named Entity Recognition (NER), Part of Speech (POS) tagging, Lemmatization, Text Similarity, Machine Translation etc. • Fine-tune ChatGPT, GPT (2, 3, 3.5turbo), LLM, BERT, Gemini, Llama, … based on the specific requirements and functionalities. • Experience training or adjusting LLMs (Hugging Face, DeepSpeed). • Strong programming skills, preferably in Python and relevant libraries like Pytorch, TensorFlow, scikit-learn, NumPy, Pandas, NLTK, spaCy, etc. which my skillset allows me to handle large datasets I believe I am the perfect fit for this project. With my skill set, you can be sure that you will receive high-quality results. If you're interested in hearing more about how I could help you, please don't hesitate to reach out! I can provide the requirements with minimum time and cost.

@mcollinscalz

Hi there, I am an experienced machine learning engineer specializing in NLP and graph-based architectures. I am well-equipped to develop your high-accuracy hate speech detection system by leveraging a robust hybrid architecture that captures both semantic content and contextual thread dynamics. Proposed Technical Strategy Feature Extraction: I will utilize pre-trained BERT (or BERTweet for better informal text handling) to generate high-fidelity embeddings. I will fine-tune these embeddings on your forum dataset to ensure the model captures domain-specific nuances, slang, and sarcasm. Structural Modeling: I will implement a Graph Attention Network (GAT) to process the forum comments. By representing the data as a graph—where nodes are comments and edges capture "reply-to" relationships or semantic similarity—the GAT will learn to weigh the influence of surrounding comments, significantly boosting classification accuracy beyond simple text-only models. Optimization: To ensure high accuracy, I will implement class-balancing techniques (weighted cross-entropy) to handle the inherent sparsity of hate speech data and optimize hyperparameters using a systematic grid or random search. Deliverables Fully commented, modular Python code (PyTorch/PyTorch Geometric). Preprocessing pipeline for raw forum comments. Training/Evaluation scripts with performance metrics (F1-score, Precision, Recall). Documentation on model architecture and tuning. Best regards,

@nexvative

Hi, I have strong experience in NLP, BERT-based language models, Graph Neural Networks (GAT/GCN), and PyTorch, and I'd be happy to implement a high-accuracy hate speech detection model for your project. The solution will use BERT embeddings to generate contextual text representations and a Graph Attention Network to capture relationships between comments and linguistic features, improving classification performance over traditional models. I'll include data preprocessing, graph construction, model training, hyperparameter tuning, evaluation, and inference scripts with clear documentation. To maximize accuracy, I'll apply appropriate preprocessing, class balancing, regularization, and fine-tuning while reporting metrics such as Accuracy, Precision, Recall, F1-score, and confusion matrix. The code will be clean, modular, and easy to extend for future datasets. You'll receive the complete source code, trained model, reproducible training pipeline, evaluation results, and documentation explaining setup and usage.

@JoseRCarvas

A BERT + Graph Attention Network pipeline for hate-speech detection on forum comments. The GAT choice is the real design question: forum comments aren't independent, so the graph has to be built deliberately (reply/thread edges, or similarity edges) for the attention to add anything over a plain BERT classifier. I'd fine-tune a BERT encoder for the comment embeddings, feed those as node features into a GAT layer, and train end to end, reporting precision/recall/F1 alongside accuracy, since accuracy alone hides the class imbalance these datasets always carry. Deliverable: commented, reproducible Python (PyTorch/HuggingFace + PyG), the training script, and an eval report. Share your labelled set if you have one; otherwise I'll use a public forum hate-speech corpus and note it. Rating 5.0 stars. Fund a milestone and I'll begin with the data + graph construction.

@alluankit01

Hi, I can implement a hate speech detection model using BERT embeddings and Graph Attention Networks in Python for forum comment data. My approach is to first clean and tokenize the comments, handle missing values and class imbalance, and generate contextual embeddings using a suitable BERT model. I’ll then construct a graph using comment relationships, reply structure, users, or semantic similarity and train a GAT classifier for hate-speech prediction. I’m experienced with Python, PyTorch, Hugging Face Transformers, BERT, graph neural networks, GAT, NLP preprocessing, model evaluation, and hyperparameter tuning. Deliverables will include: * Complete preprocessing pipeline * BERT embedding generation * Graph construction logic * GAT model implementation * Training and validation scripts * Class imbalance handling * Accuracy, precision, recall, F1, and confusion matrix * Baseline model comparison * Saved model and inference code * Clear setup documentation I’ll optimize the model carefully and report results honestly on a held-out test set rather than claiming unrealistic accuracy. The final code will be clean, reproducible, and easy to run on additional forum datasets. Best regards Ankit

@manoval09

Dear Client, Thank you for posting this project. I have extensive experience in AI automation. I'm confident I can deliver high-quality results: - Technical expertise: Proven track record with similar automation projects - Timeline: Efficient delivery with minimal delays - Support: Full testing and debugging included I'm ready to start immediately and am flexible with communication. I would appreciate the opportunity to discuss your specific requirements in detail. Best regards, Val

@Abutalha5

Hi, I am Abutalha, an AI/ML Engineer and Python Developer with experience in Python, PyTorch, TensorFlow, BERT, Graph Neural Networks (GAT/GCN), NLP, Hugging Face Transformers, deep learning, and text classification. I have worked on building and optimizing transformer-based NLP models for classification, sentiment analysis, and content moderation tasks using state-of-the-art architectures. I can implement a high-accuracy hate speech detection pipeline using BERT embeddings and Graph Attention Networks (GAT) for forum comment data. The solution will include data preprocessing, graph construction, BERT feature extraction, GAT model implementation, hyperparameter tuning, model evaluation using metrics such as Accuracy, Precision, Recall, and F1-score, along with clean, well-documented Python code that is easy to reproduce and extend. Could you share which forum comment dataset you plan to use (or whether you already have a labeled dataset), and if there are any target accuracy benchmarks or research papers you would like the implementation to follow? Best regards, Abutalha

@NEHABHAT92

Your project directly aligns with my expertise and experience. As a seasoned AI developer proficient in Python, I have consistently demonstrated a profound grasp of Natural Language Processing (NLP) and machine learning, which you have identified as key requirements for your project. In particular, I'm well-versed with BERT embeddings and Graph Attention Networks (GAT), the two components you specified need to be employed in your code. Over the past years, I have successfully executed several similar projects that required the use of BERT embeddings and complex NLP algorithms. This has not only honed my skills but also instilled in me a deep understanding of how to efficiently handle and process textual data acquired from forums such as your project's data source of forum comments. Moreover, my dedication to delivering projects that yield optimal accuracy will come in handy for the high-precision demands of your project. I guarantee not just the completion of this task but also its successful implementation towards fulfilling your purpose of hate speech detection within forum comments using BERT embeddings and GAT. I am eager to put my skills to work on your project; let’s join forces to transform your dreams into reality.

@wilfrednjorog

Hi there, I can build a BERT+GAT hate-speech detector for forum comments that targets high accuracy. I'll fine-tune BERT for embeddings, construct comment graphs (replies/semantic edges), and train GAT with class weighting and CV. I will deliver a validated model, evaluation metrics, and optimization recommendations; ready to start a 2-week prototype. Best regards, Wilfred

@CyberSasu

With an extensive background in AI and Machine Learning, particularly in the realm of hate speech detection, I am confident that I am the perfect fit for your project. I have hands-on experience using BERT embeddings and Graph Attention Networks (GAT) to tackle complex NLP tasks, such as hate speech detection. My repertoire extends to handling and processing diverse text data, including forum comments - a crucial requirement for this project. I understand that achieving high accuracy in hate speech detection is paramount. Rest assured, that's my speciality! I leverage my expertise in optimizing models to attain peak performance. Having worked on AI/ML-based smart systems, I comprehend the ethical significance of identifying and eliminating hateful language for cultivating positive online discourse. Lastly, let me assure you that collaborating with me won't just be about accomplishing the job. You can expect clear communication, reliable support, and a commitment to delivering practical and effective results. Let's come together to make the digital space a better place by tackling hate speech head-on.

@devu58

Hey, I'm AI Graduate (Masters) and I've gone through your requirement of building up the hate speech detection code using BERT and GAT. I can do this for you as I've done many projects in AI/ML and your code will be delivered with proper comments. If you have your own dataset that'll be great or I'll use the standard dataset - Jigsaw Toxic Comment Classification Challenge (Kaggle). Feel free to ping me up. Cheers ;)

@ladycat25

A BERT + Graph Attention Network (GAT) pipeline is a strong approach for hate speech detection because it captures both contextual language understanding and relationships between words or comments. My experience includes building NLP and deep learning solutions using Transformer-based models, text classification pipelines, and model optimization to improve accuracy and generalization. I can implement a complete training and inference workflow, including data preprocessing, graph construction, BERT embeddings, GAT architecture, evaluation metrics, and well-documented code for reproducible results. Before finalizing the approach, could you share the dataset details, including the number of forum comments, label distribution, language(s) used, and whether the graph structure is already defined or needs to be generated from the text?

@RyanF172

I understand that achieving high accuracy in hate speech detection using BERT embeddings and Graph Attention Networks (GAT) is crucial for your project. Leveraging my experience with BERT and GAT, along with a solid background in NLP and machine learning, I am confident in delivering results that meet your expectations. I will approach the project by implementing GAT for hate speech detection, incorporating BERT for efficient word embeddings, and processing forum comments to train the model effectively. My focus will be on optimizing the models to ensure high accuracy in hate speech detection, aligning with your project requirements. For this project, I will specifically utilize BERT embeddings, GAT, and NLP techniques to address the unique challenges posed by forum comment data. Clear communication and revisions will be integral throughout the project to ensure your needs are met effectively. Even if you don't hire me, the worst that will happen is that you'll end with a free consultation, so let's chat! Regards, Ryan Would you consider exploring additional data augmentation techniques to enhance the model's performance further?

@Rajatt1099

Hi There, I’ve reviewed your requirements and can help develop a hate speech detection model using BERT embeddings combined with Graph Attention Networks (GAT). I can assist with: • Preparing and preprocessing forum comment datasets for NLP tasks • Generating contextual embeddings using BERT models • Designing and implementing a GAT-based classification architecture • Building graph representations from text relationships/features • Training, evaluating, and optimizing the model for improved accuracy • Implementing validation metrics such as precision, recall, F1-score, and accuracy • Providing clean, documented code with setup and usage instructions My focus will be on building a robust and scalable NLP pipeline with proper experimentation, model tuning, and performance evaluation. I’d be happy to discuss your preferred dataset format, target framework (PyTorch/TensorFlow), and accuracy expectations to finalize the implementation approach. Regards, Rajat Trivedi

@roshanzag

Hello, It would be exciting to take up the task of developing your hate speech detection system using **BERT embeddings** and **Graph Attention Networks (GAT)**. I am an experienced Python developer and machine learning and NLP enthusiast and would develop a good pipeline to preprocess the forum comments, create contextual embeddings using BERT, generate graphs and train the GAT model for hate speech classification. The emphasis would be on building a neat implementation which involves good data preprocessing, model training, evaluation and tuning of hyperparameters for maximizing accuracy. Performance measures such as accuracy, precision, recall and F1 score will be provided along with instructions to run the project. I give importance to timely communication, quality work and meeting the requirements of the project. I would like to know about your dataset, accuracy required and other specifics before beginning the project. Thank you.

@himanshithakkar7

Hi, I can build this using BERT for contextual embeddings and a Graph Attention Network on top, so the model learns from both comment text and forum structure (reply chains, threads) — not just isolated text. Approach: - Clean and structure forum comments into a graph (comments/users as nodes, replies as edges) - Generate embeddings via pretrained BERT (or HateBERT if suited to your domain) - Train multi-head GAT layers on top for classification - Handle class imbalance with weighted loss, and report precision/recall/F1 alongside accuracy for an honest performance picture - Deliver documented code (PyTorch, HuggingFace, PyTorch Geometric), trained weights, and an evaluation report Quick question before I scope this properly: do you already have a labeled dataset, or does labeling need to be part of the work? And any target dataset size? Happy to share a timeline once I know that. Looking forward to it.

@shashwatv4

Hi, I'd like to work on your hate speech detection project. My approach: I'll use pretrained BERT to generate contextual embeddings for each forum comment, then construct a graph where nodes are comments and edges are built from reply/thread relationships (or semantic similarity if reply structure isn't available in your data). A Graph Attention Network will then be trained on top of these embeddings this lets the model learn not just from comment text, but from conversational context, which typically improves detection accuracy over BERT-only classifiers, especially for subtle or context dependent hate speech. I'll handle data preprocessing, graph construction, model training (PyTorch + PyTorch Geometric + HuggingFace Transformers), and evaluation using precision/recall/F1 (not just accuracy, since hate speech datasets are usually imbalanced and F1 gives a truer picture of real performance). I have hands on experience with NLP and ML pipelines including building classifiers on real-world tabular and text data with strong evaluation practices (cross validation, handling class imbalance, feature importance analysis). I can share sample work if useful. Happy to discuss your forum data format and hate speech taxonomy before starting, to make sure the model is tuned to your specific use case. Can deliver within 4 days with clean, documented code and a results report.

@SmartDevLabs

Your pipeline is clear: BERT contextual embeddings feeding a Graph Attention Network over forum comments, tuned for detection accuracy. Two upfront calls — I'd build the comment graph on reply-thread and user co-occurrence edges so GAT has real structure to attend over, and I'll track macro-F1 alongside accuracy, since hate-speech data is heavily imbalanced and raw accuracy hides false negatives. Delivery: (1) preprocess your forum comments and build the graph; (2) generate BERT embeddings and train a multi-head GAT; (3) hand you a working model with accuracy/F1 plus real per-comment predictions to check before any release; (4) iterate to your target. One question: do you already have a labeled forum dataset, or should I benchmark on a standard public set first? Straight up: we're a new account here, so I'm pricing low to earn our first five-star reviews — that's the only reason the quote sits near your budget. Payment stays in milestone escrow; you release only after you've checked the trained model's accuracy and predictions. All the risk is on my side.