waseemghanem98/ qwen3-bankassist

Updated 21 hours ago

Banking intent classification model based on Qwen3-0.6B, designed to classify customer banking messages into 77 intent categories.

ollama run waseemghanem98/qwen3-bankassist

curl http://localhost:11434/api/chat \
  -d '{
    "model": "waseemghanem98/qwen3-bankassist",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='waseemghanem98/qwen3-bankassist',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'waseemghanem98/qwen3-bankassist',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Models

Name

1 model

Size / Usage

Context

Input

qwen3-bankassist:latest

1.2GB · 40K context window · Text · 21 hours ago

qwen3-bankassist:latest

1.2GB

40K

Text

Readme

Qwen3 BankAssist

Qwen3 BankAssist is a banking intent classification model based on Qwen3-0.6B.

It classifies customer banking messages into one of 77 intent categories from the Banking77 dataset.

Run

”`bash ollama run waseemghanem98/qwen3-bankassist Example

Input:

My card still hasn’t arrived.

Output:

card_arrival Model Details Base model: Qwen/Qwen3-0.6B Task: Banking intent classification Dataset: Banking77 Intent classes: 77 Parameters: 596M Format: GGUF Precision: F16 Runtime: Ollama Evaluation

The model was evaluated on the full Banking77 test split containing 3,076 unseen examples across all 77 intent categories.

Model Accuracy Invalid predictions Qwen3-0.6B 22.07% 335 Qwen3 BankAssist 47.79% 160

The fine-tuned model improved accuracy by 25.72 percentage points and reduced invalid predictions from 335 to 160.

Evaluation used the same prompt, tokenizer, deterministic generation settings, and exact intent-label matching for both models.

Model Pipeline Banking77 ↓ Qwen3-0.6B ↓ LoRA fine-tuning ↓ Merged model ↓ GGUF ↓ Ollama Usage

The model is intended for experiments involving banking intent classification, NLP workflows, and local LLM inference.

Example messages include:

Where is my card? I forgot my PIN. Why was my transfer declined?

The expected output is a single Banking77 intent label.

Related Models

Hugging Face standalone model:

WaseemGh98/bankassist-qwen3

Hugging Face LoRA adapter:

WaseemGh98/waseem-bankassist-qwen3-lora

Limitations

The model is experimental and is not intended for production banking decisions or customer-facing financial advice.

Its measured accuracy on Banking77 is 47.79%, so predictions may be incorrect or fall outside the expected intent labels.

Attribution

Base model: Qwen/Qwen3-0.6B Base model license: Apache 2.0

Dataset: Banking77 Original dataset: PolyAI/banking77 Dataset license: CC BY 4.0

# Qwen3 BankAssist

Qwen3 BankAssist is a banking intent classification model based on Qwen3-0.6B.

It classifies customer banking messages into one of 77 intent categories from the Banking77 dataset.

## Run

```bash
ollama run waseemghanem98/qwen3-bankassist
Example

Input:

My card still hasn't arrived.

Output:

card_arrival
Model Details
Base model: Qwen/Qwen3-0.6B
Task: Banking intent classification
Dataset: Banking77
Intent classes: 77
Parameters: 596M
Format: GGUF
Precision: F16
Runtime: Ollama
Evaluation

The model was evaluated on the full Banking77 test split containing 3,076 unseen examples across all 77 intent categories.

Model	Accuracy	Invalid predictions
Qwen3-0.6B	22.07%	335
Qwen3 BankAssist	47.79%	160

The fine-tuned model improved accuracy by 25.72 percentage points and reduced invalid predictions from 335 to 160.

Evaluation used the same prompt, tokenizer, deterministic generation settings, and exact intent-label matching for both models.

Model Pipeline
Banking77
    ↓
Qwen3-0.6B
    ↓
LoRA fine-tuning
    ↓
Merged model
    ↓
GGUF
    ↓
Ollama
Usage

The model is intended for experiments involving banking intent classification, NLP workflows, and local LLM inference.

Example messages include:

Where is my card?
I forgot my PIN.
Why was my transfer declined?

The expected output is a single Banking77 intent label.

Related Models

Hugging Face standalone model:

WaseemGh98/bankassist-qwen3

Hugging Face LoRA adapter:

WaseemGh98/waseem-bankassist-qwen3-lora

Limitations

The model is experimental and is not intended for production banking decisions or customer-facing financial advice.

Its measured accuracy on Banking77 is 47.79%, so predictions may be incorrect or fall outside the expected intent labels.

Attribution

Base model: Qwen/Qwen3-0.6B
Base model license: Apache 2.0

Dataset: Banking77
Original dataset: PolyAI/banking77
Dataset license: CC BY 4.0

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