Explore a source-linked DistilBERT text-classification workflow built with PyTorch and Hugging Face Transformers. Follow dataset preparation, full fine-tuning, frozen-backbone comparison, model evaluation, and inference through executable Python and Jupyter examples.
A production-grade implementation with clearly documented data size and evaluation scope. Source code is the authority for implementation details.
01 / PROJECT OVERVIEW
The implementation uses the pretrained distilbert-base-uncased model with a four-label sequence-classification head. It prepares customer-support examples, tokenizes text, configures Hugging Face Trainer, compares training approaches, evaluates predictions, and runs inference on new support messages.
Transform free-form customer messages into structured categories to demonstrate a classification workflow for routing and analysis. Each training example contains the message text and one of four class labels.
Core dependencies include PyTorch, Transformers, Datasets, Accelerate, NumPy, Pandas and scikit-learn. The script uses automatic CPU/CUDA detection, stratified train/validation splitting and a dynamic-padding collator.
02 / WORKFLOW
Explore the main execution path represented in the Python classifier source. The steps are designed to make the training lifecycle easy to follow, reproduce and inspect.
Define 30 illustrative messages across four categories and create a stratified 80/20 split: 24 training examples and six validation examples.
Use the DistilBERT tokenizer, truncate inputs to 128 tokens and dynamically pad batches with DataCollatorWithPadding.
Initialize a pretrained DistilBERT backbone with a four-class head, a fixed label mapping and training configuration.
Train the full sequence classifier, then train a second model with the DistilBERT backbone frozen and classification-head layers trainable.
Calculate accuracy and weighted F1, generate a classification report and inspect the confusion matrix on the validation split.
Tokenize incoming text, run the trained model in inference mode and return its predicted category, top score and scores for every label.
03 / TRAINING STRATEGIES
The repository includes both configurations, making it possible to inspect differences in trainable parameters, learning rates and evaluation behavior without implying a universal winner.
Updates DistilBERT backbone parameters together with the sequence-classification head. The script evaluates each epoch and selects the best checkpoint using weighted F1.
Keeps pretrained DistilBERT encoder weights fixed while training the classifier and pre-classifier layers. It offers a clear comparison against end-to-end optimization.
Training configurations shown above are read from the repository source. The visual bars illustrate which parts are trainable; they do not represent benchmark results or exact parameter percentages.
04 / IMPLEMENTATION DETAILS
Core capabilities are visible in the Python source and supported by a notebook companion, making the project useful for understanding real model-training APIs and evaluation workflows.
Fixed random seed and stratified splitting retain representation of each support class in the small validation set.
Hugging Face tokenizer and data collator encode examples and pad each batch without globally padding every sample to the maximum length.
TrainingArguments configure epoch counts, learning rates, batch sizes, evaluation, logging and checkpoint selection.
Device detection selects available hardware; mixed precision is configured according to CUDA support and hardware capabilities.
Evaluation routines generate accuracy, weighted F1, a class-level classification report and confusion matrix.
A classify_query() function returns the predicted class, a softmax-based confidence score and per-class scores.
05 / EVALUATION & OUTPUT
The script exposes training and evaluation information instead of relying on an unsupported headline accuracy claim. Results depend on the runtime, data split and trained checkpoint.
The repository uses just six validation examples. Results from such a small sample cannot establish production-level classification quality.
A Python function produces a structured prediction for each new message. The example below shows the output fields, not a measured prediction.
06 / RUN THE PROJECT
Use the documented classifier-only dependencies and the standalone Python script. A compatible PyTorch installation is required for your CPU or CUDA environment; the first run downloads model weights unless already cached.
# Clone the repository and enter the project git clone https://github.com/su394611-del/Transformers-Training.git cd Transformers-Training # Create and activate a virtual environment py -3.12 -m venv .venv .\.venv\Scripts\Activate.ps1 # Install and run the classifier example python -m pip install --upgrade pip python -m pip install -r requirements_distilbert.txt python DistilBERT_Colab_Fixed_2026.py
The script can use CPU when CUDA is unavailable; training generally runs more slowly without a GPU.
Explore the companion notebook for an interactive workflow.
The full fine-tuning path saves its selected model and tokenizer under support_classifier/best_model/.
07 / SOURCE & DOCUMENTATION
Go directly to the source files and technical documentation. The repository remains the source of truth for code, dependencies and execution details.
08 / FREQUENT QUESTIONS
It assigns customer-support messages to billing, technical, account or feature_request categories using a four-label DistilBERT sequence classifier.
Full fine-tuning updates the pretrained DistilBERT encoder and classification head. The comparison approach keeps the pretrained encoder fixed and updates the classification-head layers, changing the number of trainable parameters and optimization behavior.
No. The classifier script can run using CPU or a compatible CUDA device. Larger datasets and training runs may benefit from GPU acceleration.
No production performance guarantee is established by this project. The source uses 30 illustrative labeled examples with only six validation rows. Evaluate on representative, sufficiently large data before using a model in a real support-routing system.
The buttons in the Source & Documentation section link directly to the Python classifier, Jupyter notebook, requirements file, README and GitHub Wiki location.