PRODUCTION-GRADE ENGINEERING PRESENTATION

Fine-tune the encoder.
Measure the difference.

A hands-on DistilBERT engineering workflow that classifies customer-support messages across four categories, compares full fine-tuning with a frozen-backbone baseline, and exposes model diagnostics through PyTorch, Hugging Face Transformers and scikit-learn.

Explore GitHub source Explore the architecture ↓

PyTorchDistilBERTTransformers TrainerWeighted F1Frozen backbone

MODEL TRAINING / WORKFLOWPYTORCH • HF TRAINER

Customer support classification

30 labeled messagesbilling · technical · account · feature request

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Stratified split · tokenizer24 training / 6 validation · dynamic padding

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Pretrained DistilBERT encoderdistilbert-base-uncased

TWO TRAINING STRATEGIES

Full fine-tuningencoder + classifier

Frozen backboneclassification layers only

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Evaluate & compareaccuracy · weighted F1 · confusion matrix

OUTPUT: FOUR SUPPORT CLASSESINFERENCE + CHECKPOINT

4Support ticket classes

2Training strategies

30Hand-written example records

128Maximum tokens per input

IMPLEMENTATION CAPABILITIES

From tokenization to model evaluation.

Source-backed capabilities cover the full classifier workflow: data preparation, model optimization, diagnostic evaluation, checkpoint output and sample inference.

Pretrained DistilBERT

Initialize distilbert-base-uncased through Hugging Face AutoModelForSequenceClassification with four labeled outputs.

Full-parameter fine-tuning

Update the pretrained encoder and the classification layers together using the Trainer API.

Frozen-backbone baseline

Freeze the encoder while training both the pre-classifier and classifier layers for a controlled comparison.

Stratified data splitting

Build a DatasetDict from 30 labeled support messages, with a 24/6 train/validation partition.

Tokenization and dynamic padding

Apply the DistilBERT tokenizer with 128-token truncation, then pad each batch with DataCollatorWithPadding.

Training and checkpointing

Use configurable TrainingArguments, AdamW-based optimization, scheduled evaluation, and best-model checkpoint selection.

Accuracy and weighted F1

Compute metrics using scikit-learn, including class-weighted F1 for a consistent comparison of the two strategies.

Classification diagnostics

Generate a full classification report and confusion matrix to inspect the four output classes.

Inference with scores

Classify example queries using softmax probabilities and report the predicted class alongside per-class scores.

CPU or optional CUDA

Detect the available PyTorch device and select BF16 or FP16 where supported on NVIDIA CUDA hardware.

Python and notebook entry points

Use the standalone script or run the matching Jupyter Notebook in VS Code, JupyterLab, or Google Colab.

Compatibility handling

Adapt to selected Hugging Face Trainer and TrainingArguments signature differences across supported versions.

Four output categories

BillingCharges, invoices, subscription payments

TechnicalApplication failures, API errors, access issues

AccountProfile, ownership, access and account changes

Feature requestProduct additions, integrations and usability ideas

HIGH-LEVEL SYSTEM DESIGN

Two training paths. One evaluation framework.

Both experiments start with the same data preparation and pretrained DistilBERT checkpoint. The difference is which parameters receive gradient updates.

Full-model fine-tuning

TRAIN ALL LAYERS

Tokenize the support messagesDistilBERT tokenizer, truncation, dynamic padding

Initialize the pretrained encoderFour-class sequence classification head

Optimize encoder + classifier4 epochs, learning rate 2e-5

Select and save the checkpointBest weighted F1 on the example validation set

Updates every trainable model parameter and includes detailed class-level evaluation and example predictions.

Frozen-backbone baseline

TRAIN HEAD ONLY

Reuse the same prepared dataSame category mapping and evaluation split

Load DistilBERT separatelyFreeze all DistilBERT encoder parameters

Train the classification layers6 epochs, learning rate 1e-3

Evaluate the baselineAccuracy and weighted F1 for comparison

Retains pretrained encoder weights while updating pre_classifier and classifier; demonstrates the tradeoff between adaptation and trainable parameter count.

Architecture boundary: This is sequence classification using an existing pretrained encoder. The repository does not implement foundation-model pre-training or mid-training, despite those words appearing in its repository name.

TRAINING STRATEGY & METRICS

A transparent comparison of fine-tuning approaches.

Configurations below come from the checked-in Python source. Metrics are calculated at runtime; no benchmark result is asserted without a verified run.

STRATEGY A · FULL

Full DistilBERT fine-tuning

Fine-tune the pretrained encoder together with the classification head. The workflow tracks validation F1, selects the best checkpoint, and produces inference scores for example support queries.

Four training epochs; batch size eight
Learning rate 2e-5; weight decay 0.01
Epoch-level evaluation and checkpoint selection
Accuracy, weighted F1, report and confusion matrix

STRATEGY B · FROZEN ENCODER

Classification-head baseline

Preserve the DistilBERT representation layers and optimize only the classification components. The same data split supports a focused comparison of downstream task adaptation.

Six training epochs; batch size eight
Learning rate 1e-3
Encoder parameters frozen; head parameters trainable
Accuracy and weighted F1 evaluation

Parameter	Full fine-tuning	Frozen backbone
Pretrained checkpoint	`distilbert-base-uncased`	`distilbert-base-uncased`
Trainable parameters	Encoder and classification head	`pre_classifier` and `classifier`
Epoch count	4	6
Learning rate	`2e-5`	`1e-3`
Per-device batch size	8	8
Maximum sequence length	128 tokens	128 tokens
Validation	Accuracy, weighted F1, per-class report, confusion matrix	Accuracy and weighted F1
Artifact output	`support_classifier/best_model/`	Training outputs in `support_classifier_frozen/`

Accuracy

Fraction of validation messages assigned the correct category. Small evaluation sets can make this metric unstable.

Weighted F1

Combines precision and recall across classes, weighted by each category’s support in the validation data.

Confusion matrix

Displays actual versus predicted categories to reveal the types of classification mistakes, not just a single score.

RUN THE SOURCE

A reproducible local workflow.

Use the real filenames on GitHub. The script runs full fine-tuning and then the frozen-backbone experiment in sequence. A pretrained model download is needed on the first run unless cached.

01 · Clone and prepare Python

git clone https://github.com/su394611-del/Full-Fine-Training-Pre-Training-Mid-Training.git
cd Full-Fine-Training-Pre-Training-Mid-Training
py -3.12 -m venv .venv
.\.venv\Scripts\python.exe -m pip install --upgrade pip

02 · Install dependencies

.\.venv\Scripts\python.exe -m pip install -r requirements.txt
.\.venv\Scripts\python.exe -m pip check

03 · Train and evaluate both strategies

.\.venv\Scripts\python.exe Full_Fine_Tuning_Using_DistilBERT.py

04 · Check PyTorch device availability

.\.venv\Scripts\python.exe -c "import torch; print(torch.__version__); print(torch.version.cuda); print(torch.cuda.is_available())"

05 · Start JupyterLab (optional)

.\.venv\Scripts\python.exe -m jupyter lab
# Open Full_Fine_Tuning_Using_DistilBERT.ipynb

Production evaluation boundary: The code implements core fine-tuning, evaluation and checkpoint patterns. Its 30 hand-written examples, including only six validation records, cannot substantiate deployment-grade generalization, reliability, safety or classification quality. No independent production benchmark or scale test is claimed here.

SOURCE CODE & DOCUMENTATION

Review the implementation directly.

These links point to real files in the current public repository. The separate GitHub Wiki is provided as an additional documentation destination.

Fine-tune the encoder.Measure the difference.

From tokenization to model evaluation.

Pretrained DistilBERT

Full-parameter fine-tuning

Frozen-backbone baseline

Stratified data splitting

Tokenization and dynamic padding

Training and checkpointing

Accuracy and weighted F1

Classification diagnostics

Inference with scores

CPU or optional CUDA

Python and notebook entry points

Compatibility handling

Four output categories

Two training paths. One evaluation framework.

Full-model fine-tuning

Frozen-backbone baseline

A transparent comparison of fine-tuning approaches.

Full DistilBERT fine-tuning

Classification-head baseline

Accuracy

Weighted F1

Confusion matrix

A reproducible local workflow.

Review the implementation directly.

Repository files

Fine-tune the encoder.
Measure the difference.