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