LLM for Hierarchy & Toxicity

@proy89

Hi there, I can definitely build this dual-task classifier for you. Fine-tuning LLMs for multi-label classification is something I've done before, and your pipeline requirements are clear. 5+ years full-stack experience with Python, PyTorch, and transformer models I'll use a lightweight base model (like DistilBERT or a smaller BERT variant) to hit the 150 samples/sec target on A100 while still achieving 0.80+ macro-F1 The CSV ingestion, cleaning, and training loop will be fully automated so you can drop in new files I'll deliver both the training pipeline and a fast inference script with your two output predictions per sample Clear README with env setup, CLI usage, and extension guides for adding hierarchy levels One thing I'd like to clarify: roughly how many hierarchy levels should the model distinguish beyond just junior/senior? And do you have an estimate of total training samples in your CSV? Looking forward to working on this with you. Thanks

@ashinnd84

Hey there, we are a team of developers and we can do this project in no time. Please, send me a message to discuss the work. Thanks Ashish Kumar.

@devanshkhandekar

Hi, I’m a seasoned Applied ML Engineer (6+ yoe) specialized in building practical NLP pipelines for classification & decision support & I can deliver a high-performance system that meets your >150 samples/sec requirement using a specialized modeling strategy My Approach: -Data Audit:Review CSV labels,class balance & text noise to identify risks in toxicity & hierarchy definitions -Baseline Development:Establish TF-IDF & transformer baselines to verify learnability before complex modeling -Multi-Task Fine-Tuning:Train an encoder-based transformer for joint toxicity & hierarchy prediction with specialized scoring heads -Performance Optimization:Use class weighting & threshold tuning to maximize Macro-F1 while ensuring high inference speeds -Production:Deliver a reproducible Python pipeline for direct CSV processing & easy future label extension -Strategy Note:I recommend a fine-tuned encoder model over generative LLMs to ensure the stability & low-latency performance required for your high-throughput needs Relevant experience: -built production ML pipelines for classification & anomaly-detection style tasks where label quality,class imbalance & evaluation rigor mattered -worked on NLP/LLM systems involving structured extraction,semantic classification & reproducible training/inference workflows -built applied data science solutions in churn prediction,anomaly detection & decision-support analytics where the real work was turning noisy raw data into reliable model outputs

@akkiwadhwa1995

Hi, This is a great fit for my experience building LLM-powered classification systems and data pipelines. I can fine-tune a transformer model (e.g., BERT/DeBERTa) to jointly predict: • Toxicity (multi-class or score-based) • Organizational hierarchy (senior → junior detection) **Approach:** • Load CSV directly (no extra preprocessing needed from your side) • Clean and normalize text (light preprocessing to preserve semantics) • Fine-tune using HuggingFace Transformers with multi-task learning • Optimize for both accuracy (Macro-F1 ≥ 0.80) and speed (≥150 samples/sec on A100 via batching + FP16) **Deliverables:** • Reproducible training script (Python + PyTorch) • Fine-tuned model weights • Fast inference script (batch + GPU optimized) • Outputs: toxicity score/label + hierarchy classification per text • Evaluation report (Precision, Recall, F1 per task) • Clear README (setup, usage, extending labels) I’ve built similar pipelines for large-scale text classification and AI enrichment systems, so I’ll ensure clean code, reproducibility, and production-ready performance. Happy to review your definitions/sample data before starting. Thanks, Akshay

@Geddawy2

Dual-task transformer fine-tune — toxicity scoring + hierarchy detection — delivered as a reproducible Python pipeline with ONNX-optimized inference hitting ≥150 samples/sec. That's exactly what I'll build. Here's the approach: Base model: deberta-v3-base (strong on nuanced text classification) with two output heads — one for toxicity rating, one for senior/junior hierarchy detection Training: HuggingFace Transformers + PyTorch, reading your CSVs directly, no preprocessing step on your end Optimization: ONNX Runtime quantization to push inference speed well past your 150 samples/sec threshold on A100 — I've done this before and cut latency by 50% on a production NER model Evaluation: Macro-F1 reported per task on your held-out CSV, targeting ≥0.80 on both heads I'm an AI engineer currently building production NLP pipelines — most recently a privacy/PII classifier (Qwen-3B-Instruct) and a multi-label code classification system achieving 93% accuracy, both CSV-driven with F1-based evaluation. This project is the same pattern, just with your domain definitions plugged in. I'd like to see 20–30 sample rows and your toxicity/hierarchy definitions before we start — not to qualify the project, but to confirm label distribution and finalize the base model choice so there are no surprises at validation time. All four deliverables (training script, weights + inference notebook, README, eval report) ready for handoff.

@Hrolgar

Formal definitions do most of the work here. If hierarchy and toxicity aren't operationalized precisely before prompting, you get inconsistent labels regardless of model choice. My approach: structured output schema aligned to your definitions, 20-30 few-shot examples drawn from your corpus, and a baseline run with Qwen or Llama before touching any paid API. You also get an eval set you label once and reuse to track accuracy as prompts evolve. Deliverable is a Python library with CLI so classification runs on new batches without touching internals. M1: Definitions + schema + few-shot set, INR 3,150, 3d. M2: Model integration + eval loop, INR 3,150, 4d. M3: CLI library + accuracy report, INR 3,200, 3d. What does your corpus look like, and do you have any labeled examples already?

@ssldevmaster

Hi, I can easily DO your work IN 24 HOURS, DM me now to get started, PRICE NEGOTIABLE 100% Work satisfaction is provided

@manoval09

Hi, You need a text classifier that respects your formal definitions of organizational hierarchy and toxicity—not generic, out-of-the-box models, but something calibrated to how you actually define these concepts. That's the hard part: most off-the-shelf LLMs won't distinguish subtle hierarchy signals your definition captures, and toxicity varies by context and domain. I'd build this using Python with a fine-tuned transformer (DistilBERT or similar) on your labeled data, then expose it via FastAPI for inference. Your formal definitions become the ground truth in the training split; the model learns to recognize patterns *you* care about. This approach stays within budget, runs fast, and avoids per-request cloud API costs. First step: send me your definitions and a few text samples you'd classify, and I'll sketch out the data pipeline and model architecture in 24 hours. Does that scope feel right, or do you need the classifier to handle streaming data or integrate with a specific backend? Best regards, Val

@yasinu8

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