intent-banking

Qwen2.5-7B Banking Intent Classification

This is a LoRA adapter fine-tuned on the BANKING77 dataset to perform fine-grained intent classification in the banking domain. The model is based on unsloth/Qwen2.5-7B and trained using the Unsloth library for highly efficient training.

Model Details

• Model Type: Causal Language Model with LoRA adapter
• Developer: ngbaoan
• Base Model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
• Language: English
• Task: Intent Classification
• Dataset: BANKING77 (77 distinct banking-related intents)

Performance

The model was evaluated on the test set and achieved the following results:

• Accuracy: 92.29% (0.9229)
• Macro F1-Score: 0.85
• Weighted F1-Score: 0.92

(Note: Some labels in the dataset subset might have 0 support, which affects the macro average. For supported intents, the F1 score ranges from 0.80 to 1.00).

Intended Use

This model is designed to classify user queries related to banking operations (e.g., card activation, lost cards, top-up failures, exchange rates, etc.) into one of 77 specific intents.

Example Input:

"I tried to top up my account using a card but it failed, what should I do?"

Example Output:

top_up_failed

Training Details

The model was fine-tuned efficiently using Unsloth with 4-bit quantization and LoRA.

Training Hyperparameters

• LoRA Rank (r): 64
• LoRA Alpha: 64
• Batch Size: 2 (per device)
• Gradient Accumulation Steps: 4
• Learning Rate: 5.0e-5
• Optimizer: adamw_8bit
• LR Scheduler: cosine
• Warmup Steps: 20
• Weight Decay: 0.01
• Epochs: 6
• Max Sequence Length: 512

How to Get Started with the Model

Since this is a LoRA adapter, you need to load the base model and then apply these PEFT weights. The easiest way is using the unsloth library or standard transformers.

from unsloth import FastLanguageModel
import torch

max_seq_length = 512

# 1. Load the model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "ngbaoan/intent-banking", # Your Hugging Face repo
    max_seq_length = max_seq_length,
    dtype = None,
    load_in_4bit = True,
)
FastLanguageModel.for_inference(model)

# 2. Format your prompt
prompt = """Instruct: Classify the following banking query into the correct intent.
Query: I lost my card yesterday and I need a replacement.
Intent: """

inputs = tokenizer([prompt], return_tensors = "pt").to("cuda")

# 3. Generate the response
outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
print(tokenizer.batch_decode(outputs, skip_special_tokens = True)[0])

Framework Versions

• PEFT 0.18.1
• Transformers
• Unsloth
• TRL