hblim
/

bert-customer-complaints-classifier

@@ -1,199 +1,108 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+license: mit
+datasets:
+- hblim/customer-complaints
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- google-bert/bert-base-uncased
+tags:
+- bert
+- transformers
+- customer-complaints
+- text-classification
+- multiclass
+- huggingface
+- fine-tuned
+- wandb
 ---
+# BERT Base (Uncased) Fine-Tuned on Customer Complaint Classification (3 Classes)
+## 🧾 Model Description
+This model is a fine-tuned version of [`bert-base-uncased`](https://huggingface.co/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](https://wandb.ai/), and all hyperparameters are reproducible and logged below.
+---
+## 🧠 Intended Use
+- 🏷 Classify customer complaint text into 3 predefined categories
+- 📊 Analyze complaint trends over time
+- 💬 Serve as a backend model for customer service applications
+---
+## 📚 Dataset
+- Dataset Name: [hblim/customer-complaints](https://huggingface.co/datasets/hblim/customer-complaints)
+- Dataset Type: Multiclass text classification
+- Classes: billing, product, delivery
+- Preprocessing: Standard BERT tokenization
+---
+## ⚙️ 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
+---
+## 📈 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`](https://wandb.ai/notslahify/customer%20complaints%20fine%20tuning/runs/c75ddclr)
+| Step | Training Loss | Validation Loss | Accuracy   |
+|------|---------------|-----------------|------------|
+| 100  | 1.106100      | 1.040519        | 0.523810   |
+| 200  | 0.944800      | 0.744273        | 0.738095   |
+| 300  | 0.660000      | 0.385309        | 0.900000   |
+| 400  | 0.412400      | 0.273423        | 0.904762   |
+| 500  | 0.220800      | 0.185636        | 0.923810   |
+| 600  | 0.163400      | 0.245850        | 0.919048   |
+| 700  | 0.116100      | 0.180523        | 0.942857   |
+| 800  | 0.097200      | 0.254475        | 0.928571   |
+| 900  | 0.052200      | 0.233583        | 0.942857   |
+| 1000 | 0.050700      | 0.223150        | 0.928571   |
+| 1100 | 0.035100      | 0.271416        | 0.919048   |
+| 1200 | 0.027700      | 0.226478        | 0.933333   |
+| 1300 | 0.009000      | 0.218807        | 0.938095   |
+| 1400 | 0.013600      | 0.246330        | 0.928571   |
+| 1500 | 0.014500      | 0.226987        | 0.933333   |
+---
+## 🚀 How to Use
+```python
+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()