README.md

---
datasets:
- bitext/Bitext-travel-llm-chatbot-training-dataset
language:
- en
metrics:
- accuracy
- f1
- recall
- precision
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: text-classification
library_name: adapter-transformers
---
# DistilBERT with LoRA for Intent Recognition

This is a parameter-efficient fine-tuned version of `distilbert-base-uncased` using the LoRA technique via [PEFT](https://github.com/huggingface/peft). The model was trained for intent recognition using a custom dataset.

## 🧾 Model Details
- Base model: `distilbert-base-uncased`
- Fine-tuning method: [LoRA (Low-Rank Adaptation)](https://arxiv.org/abs/2106.09685) using 🤗 PEFT
- Task: Intent Recognition (Text Classification)

## 🧠 Intended Use
You can use this model to classify user intents in applications like chatbots, virtual assistants, or voice-based interfaces.

## 🏷️ Intent Labels

This model supports classification over **33 intent labels**, including:

- BAGGAGE: check_baggage_allowance
- BOARDING_PASS: get_boarding_pass, print_boarding_pass
- CANCELLATION_FEE: check_cancellation_fee
- CHECK_IN: check_in
- CONTACT: human_agent
- FLIGHT: book_flight, cancel_flight, change_flight, check_flight_insurance_coverage, check_flight_offers, check_flight_prices, check_flight_reservation, check_flight_status, purchase_flight_insurance, search_flight, search_flight_insurance
- PRICES: check_trip_prices
- REFUND: get_refund
- SEAT: change_seat, choose_seat
- TIME: check_arrival_time, check_departure_time
- TRIP: book_trip, cancel_trip, change_trip, check_trip_details, check_trip_insurance_coverage, check_trip_offers, check_trip_plan, check_trip_prices, purchase_trip_insurance, search_trip, search_trip_insurance


## 📥 How to Use

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from peft import PeftModel
import joblib

# Load tokenizer and base model
tokenizer = AutoTokenizer.from_pretrained("hopjetair/intent")
base_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased",num_labels=33)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "hopjetair/intent")

# Load label encoder
label_encoder = joblib.load("label_encoder.pkl")

# Inference
text = "Book me a flight to New York"
clf = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = clf(text)[0]
label_num = int(result["label"].split("_")[-1])
# Convert back to label
predicted_label = label_encoder.inverse_transform([label_num])[0]
print(predicted_label)
```

## 🧪 CPU vs GPU Inference (Approximate Benchmarks)

> On average hardware — actual performance may vary based on your system configuration.

| **Task**                  | **CPU Inference Time** | **GPU Inference Time** |
|---------------------------|------------------------|-------------------------|
| Single sentence inference | ~100–200 ms            | ~5–10 ms                |
| Batch of 32 inputs        | ~2–3 seconds total     | ~100–300 ms total       |

---

## 🖥️ Minimum Requirements for CPU Inference

You can run DistilBERT inference on:

- ⚙️ Modern desktop or laptop CPUs
- ☁️ Cloud VMs (e.g., AWS `t3.medium`, GCP `e2-standard`)
- 🧩 Even on low-end devices (with some latency trade-off)