bert-customer-complaints-classifier

BERT Base (Uncased) Fine-Tuned on Customer Complaint Classification (3 Classes)

🧾 Model Description

This model is a fine-tuned version of bert-base-uncased using Hugging Face Transformers on a custom dataset of customer complaints. The task is multi-class text classification, where each complaint is categorized into one of three classes.

The model is intended to support downstream tasks like complaint triage, issue type prediction, or support ticket classification.

Training and evaluation were tracked using Weights & Biases, and all hyperparameters are reproducible and logged below.

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🧠 Intended Use

• 🏷 Classify customer complaint text into 3 predefined categories
• 📊 Analyze complaint trends over time
• 💬 Serve as a backend model for customer service applications

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📚 Dataset

• Dataset Name: hblim/customer-complaints
• Dataset Type: Multiclass text classification
• Classes: billing, product, delivery
• Preprocessing: Standard BERT tokenization

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⚙️ Training Details

• Base Model: bert-base-uncased
• Epochs: 10
• Batch Size: 1
• Learning Rate: 1e-5
• Weight Decay: 0.05
• Warmup Ratio: 0.20
• LR Scheduler: linear
• Optimizer: AdamW
• Evaluation Strategy: every 100 steps
• Logging: every 100 steps
• Trainer: Hugging Face Trainer
• Hardware: Single NVIDIA GeForce RTX 3080 GPU

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📈 Metrics

Evaluation was tracked using:

• Accuracy

To reproduce metrics and training logs, refer to the corresponding W&B run: Weights & Biases Run - baseline-hf-hub

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🚀 How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained("your-username/baseline-hf-hub")
tokenizer = AutoTokenizer.from_pretrained("your-username/baseline-hf-hub")

inputs = tokenizer("I want to report an issue with my account", return_tensors="pt")
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(dim=-1).item()