Qwen2.5-3B-blog-key-points

Qwen2.5-3B-blog-key-points

This model is fine-tuned from Qwen/Qwen2.5-3B-Instruct on the ncls-p/blog-key-points. It specializes in extracting key points from blog articles and web content, providing concise bullet-point summaries that capture the essential information.

Model Description

Qwen2.5-3B-blog-key-points is a 3B parameter model fine-tuned specifically for the task of extracting key points from articles. It can process a full article and generate a concise, bullet-point summary highlighting the most important information.

Model Details

• Model Type: Qwen2.5 (3B parameters)
• Base Model: Qwen/Qwen2.5-3B-Instruct
• Training Dataset: ncls-p/blog-key-points
• Language: English
• License: CC-BY-4.0
• Finetuning Approach: Instruction fine-tuning on article-summary pairs

Uses

Direct Use

This model is designed for extracting key points from articles. You can use it directly for:

• Summarizing blog posts
• Extracting important information from news articles
• Creating bullet-point summaries of long-form content
• Generating concise overviews of research papers

Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ncls-p/Qwen2.5-3B-blog-key-points"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

article = """
[Your article text here]
"""

prompt = f"""
Extract the key points from the following article:

{article}
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=1024)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Training

The model was fine-tuned on the blog-key-points dataset, which contains 200 article-summary pairs. Each pair consists of a full article and a bullet-point summary of key points extracted using AI.

Training Procedure

• Fine-tuning Framework: Unsloth
• Training Data Format:

  {
    "instruction": "",
    "input": "Full article content",
    "output": "Here are the key points of the article:\n* Key point 1\n* Key point 2\n* Key point 3\n..."
  }
  

Evaluation

The model was evaluated on its ability to extract relevant key points from articles not seen during training. Evaluation metrics focused on:

1. Relevance: How well the extracted points capture the main ideas of the article
2. Conciseness: The ability to summarize information in a clear, bullet-point format
3. Completeness: Whether all important information is captured in the summary

Limitations and Biases

• The model may inherit biases present in the training data, including potential biases in the source articles or in the key point extraction process.
• Performance may vary depending on the length, complexity, and domain of the input article.
• The model is primarily trained on English-language content and may not perform well on content in other languages.
• As with any summarization model, there is a risk of omitting important information or misrepresenting the original content.

How to Cite

If you use this model in your research, please cite:

@misc{qwen25-3b-blog-key-points,
  author = {ncls-p},
  title = {Qwen2.5-3B-blog-key-points},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face model repository},
  howpublished = {\url{https://huggingface.co/ncls-p/Qwen2.5-3B-blog-key-points}},
}

Dataset Creation

The dataset used to train this model was created using the llm-to-blog-key-points-dataset, a CLI tool that extracts key points from web articles using AI and adds them to a dataset in a structured format.