metadata
base_model:
- Qwen/Qwen3-0.6B
license: mit
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
library_name: transformers
tags:
- finance
- sentiment-analysis
- lora
- qwen
- financial-news
🏦 Qwen3-0.6B - Financial Sentiment Classification (v2)
Model name: YorkFr/financial-sentiment-qwen3-v2
Base model: Qwen/Qwen3-0.6B
Fine-tuning type: LoRA (merged full model)
Task: Financial news sentiment classification (positive, neutral, negative)
🧠 Overview
This model is a fine-tuned version of Qwen3-0.6B, specialized for financial sentiment analysis.
It classifies a short piece of financial or economic news into one of three categories:
- 🟢
positive— good market news, growth, profit, increase, etc. - ⚪
neutral— balanced or uncertain tone. - 🔴
negative— bad market news, loss, risk, decline, etc.
Training was performed using LoRA (PEFT) with a small balanced dataset of financial headlines and statements.
💡 Usage
You can load and use the model directly with 🤗 transformers — no PEFT or special tokenizer required.
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("YorkFr/financial-sentiment-qwen3-v2")
tokenizer = AutoTokenizer.from_pretrained("YorkFr/financial-sentiment-qwen3-v2")
text = (
"Instruction: Classify the sentiment of the following financial news sentence "
"as one of [positive, neutral, negative].\n"
"Sentence: Apple announces strong pre-orders.\n"
"Answer:"
)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=5)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))