base_model, license, language, metrics, pipeline_tag, library_name, tags

base_model	license	language	metrics	pipeline_tag	library_name	tags
Qwen/Qwen3-0.6B	mit	en	accuracy	text-classification	transformers	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)

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🧠 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.

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💡 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))