Neural-Hacker

This is a binary text classification model built on top of distilbert-base-uncased. It has been fine-tuned to distinguish between AI-generated and human-written text. Model is fine-tuned on a small custom dataset of ~1.4k samples Load the model and tokenizer with the Hugging Face Transformers library, provide a text input, and the model will output a label indicating whether the text is AI-generated or human-written. NOTE: This model is experimental and not intended for production use

DistilBERT NEET Biology MCQ Classifier (NEETBioBERT) This model is a fine-tuned version of DistilBERT (base uncased) specifically trained to classify the correct option for NEET-style multiple-choice biology questions. It selects the best answer among four choices (A, B, C, D). ------------------------------------------------------------------------- Training Data Domain: NEET (Undergraduate Medical Entrance Exam) – Biology Format: Each question has 4 options with one correct answer ------------------------------------------------------------------------- Training Configuration ------------------------------------------------------------------------- Results ------------------------------------------------------------------------- Limitations Trained on a relatively small dataset (793 questions). Limited to NEET-level biology content; not suitable for physics or chemistry. ------------------------------------------------------------------------- Intended Use Baseline model for future fine-tuning with larger datasets ------------------------------------------------------------------------- NOTE: Not recommended as a final exam-ready solution without further fine-tuning and validation. ------------------------------------------------------------------------- License: MIT

A LoRA fine-tuned version of Qwen3-0.6B, trained on a math reasoning dataset (NVIDIA Math) to improve step-by-step problem-solving and numerical answer extraction. Task: Mathematical reasoning (percentages, geometry, arithmetic) Math tutoring, reasoning and explanation generation Not intended for high-stakes or safety-critical use.