I will fine tune bert for sentiment classification and inference

A
ahmed_ml22
A
ahmed_ml22
M Ahmed Imtiaz
fine tune bert for sentiment classification and inference
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fine tune bert for sentiment classification and inference
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fine tune bert for sentiment classification and inferencefine tune bert for sentiment classification and inferencefine tune bert for sentiment classification and inference

About this gig

I will fine-tune a BERT (Hugging Face) model for sentiment or text classification tasks and deliver a ready-to-use model checkpoint, tokenizer, inference script, and evaluation notebook. This includes data preprocessing, label encoding, training loop, evaluation metrics (precision/recall/F1), and instructions to run inference locally.

Deliverables:

  • Trained model checkpoint and tokenizer (PyTorch/Hugging Face format)
  • Jupyter notebook showing training & evaluation steps
  • predict.py for easy inference (load model + tokenizer)
  • Short evaluation report and suggestions to improve performance (data augmentation, class balancing, hyperparameters)


  • AI engine

    • GPT
    • TensorFlow
    • Bert

  • Programming language

    • Python

Get to know M Ahmed Imtiaz

M Ahmed Imtiaz
  • FromPakistan
  • Member sinceSep 2025

  • Languages

    Urdu, English
I’m an ML engineer who builds fast, production-ready prototypes and clean data pipelines. My hands-on experience includes BERT fine-tuning for sentiment and text classification, graph-based recommenders (Node2Vec / GraphSAGE), and wrapping models into FastAPI endpoints with Docker. I focus on reproducible deliverables: Jupyter notebooks, model checkpoints, inference scripts, and clear run instructions so clients can validate and extend work easily. For example, in my DamTechhub internship I improved a GraphSAGE recommender’s validation AUC and produced a working FastAPI inference endpoint.

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