--- pipeline_tag: text-generation language: - multilingual inference: false license: cc-by-nc-4.0 library_name: transformers ---

Trained by Jina AI.

[Blog](https://jina.ai/news/readerlm-v2-frontier-small-language-model-for-markdown-and-json) | [Colab](https://colab.research.google.com/drive/1FfPjZwkMSocOLsEYH45B3B4NxDryKLGI?usp=sharing) # ReaderLM-v2 `ReaderLM-v2` is the second generation of Jina ReaderLM, a **1.5B** parameter language model that converts raw HTML into beautifully formatted markdown or JSON with superior accuracy and improved longer context handling. It supports multiple languages (29 in total) and is specialized for tasks involving HTML parsing, transformation, and text extraction. ## Model Overview - **Model Type**: Autoregressive, decoder-only transformer - **Parameter Count**: ~1.5B - **Context Window**: Up to 512K tokens (combined input and output) - **Supported Languages**: English, Chinese, Japanese, Korean, French, Spanish, Portuguese, German, Italian, Russian, Vietnamese, Thai, Arabic, and more (29 total) ## What's New in `ReaderLM-v2` `ReaderLM-v2` features several significant improvements over its predecessor: - **Better Markdown Generation**: Generates cleaner, more readable Markdown output. - **JSON Output**: Can produce JSON-formatted text, enabling structured extraction for further downstream processing. - **Longer Context Handling**: Can handle up to 512K tokens, which is beneficial for large HTML documents. - **Multilingual Support**: Covers 29 languages for broader application across international web data. --- # Usage Below, you will find instructions and examples for using `ReaderLM-v2` locally using the Hugging Face Transformers library. For a more hands-on experience in a hosted environment, see the [Google Colab Notebook](https://colab.research.google.com/drive/1FfPjZwkMSocOLsEYH45B3B4NxDryKLGI?usp=sharing). ## On Google Colab The easiest way to experience `ReaderLM-v2` is by running our [Colab notebook](https://colab.research.google.com/drive/1FfPjZwkMSocOLsEYH45B3B4NxDryKLGI?usp=sharing), The notebook demonstrates HTML-to-markdown conversion, JSON extraction, and instruction-following using the HackerNews frontpage as an example. The notebook is optimized for Colab's free T4 GPU tier and requires `vllm` and `triton` for acceleration and running. Feel free to test it with any website. For HTML-to-markdown tasks, simply input the raw HTML without any prefix instructions. However, JSON output and instruction-based extraction require specific prompt formatting as shown in the examples. ## Local Usage To use `ReaderLM-v2` locally: 1. Install the necessary dependencies: ```bash pip install transformers ``` 2. Load and run the model: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import re device = "cuda" # or "cpu" tokenizer = AutoTokenizer.from_pretrained("jinaai/ReaderLM-v2") model = AutoModelForCausalLM.from_pretrained("jinaai/ReaderLM-v2").to(device) ``` 3. (Optional) Pre-clean your HTML to remove scripts, styles, comments, to reduce the noise and length of the input a bit (i.e. make it more friendly for GPU VRAM): ```python # Patterns SCRIPT_PATTERN = r'<[ ]*script.*?\/[ ]*script[ ]*>' STYLE_PATTERN = r'<[ ]*style.*?\/[ ]*style[ ]*>' META_PATTERN = r'<[ ]*meta.*?>' COMMENT_PATTERN = r'<[ ]*!--.*?--[ ]*>' LINK_PATTERN = r'<[ ]*link.*?>' BASE64_IMG_PATTERN = r']+src="data:image/[^;]+;base64,[^"]+"[^>]*>' SVG_PATTERN = r'(]*>)(.*?)(<\/svg>)' def replace_svg(html: str, new_content: str = "this is a placeholder") -> str: return re.sub( SVG_PATTERN, lambda match: f"{match.group(1)}{new_content}{match.group(3)}", html, flags=re.DOTALL, ) def replace_base64_images(html: str, new_image_src: str = "#") -> str: return re.sub(BASE64_IMG_PATTERN, f'

', html) def clean_html(html: str, clean_svg: bool = False, clean_base64: bool = False): html = re.sub(SCRIPT_PATTERN, '', html, flags=re.IGNORECASE | re.MULTILINE | re.DOTALL) html = re.sub(STYLE_PATTERN, '', html, flags=re.IGNORECASE | re.MULTILINE | re.DOTALL) html = re.sub(META_PATTERN, '', html, flags=re.IGNORECASE | re.MULTILINE | re.DOTALL) html = re.sub(COMMENT_PATTERN, '', html, flags=re.IGNORECASE | re.MULTILINE | re.DOTALL) html = re.sub(LINK_PATTERN, '', html, flags=re.IGNORECASE | re.MULTILINE | re.DOTALL) if clean_svg: html = replace_svg(html) if clean_base64: html = replace_base64_images(html) return html ``` 4. Create a prompt for the model: ```python def create_prompt(text: str, tokenizer=None, instruction: str = None, schema: str = None) -> str: """ Create a prompt for the model with optional instruction and JSON schema. """ if not instruction: instruction = "Extract the main content from the given HTML and convert it to Markdown format." if schema: # This is an example instruction for JSON output instruction = "Extract the specified information from a list of news threads and present it in a structured JSON format." prompt = f"{instruction}\n```html\n{text}\n```\nThe JSON schema is as follows:```json{schema}```" else: prompt = f"{instruction}\n```html\n{text}\n```" messages = [ { "role": "user", "content": prompt, } ] return tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) ``` ### HTML to Markdown Example ```python # Example HTML html = "

Hello, world!

" html = clean_html(html) input_prompt = create_prompt(html) inputs = tokenizer.encode(input_prompt, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08) print(tokenizer.decode(outputs[0])) ``` ### Instruction-Focused Extraction ```python instruction = "Extract the menu items from the given HTML and convert it to Markdown format." input_prompt = create_prompt(html, instruction=instruction) inputs = tokenizer.encode(input_prompt, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08) print(tokenizer.decode(outputs[0])) ``` ### HTML to JSON Example ```python schema = """ { "type": "object", "properties": { "title": { "type": "string" }, "author": { "type": "string" }, "date": { "type": "string" }, "content": { "type": "string" } }, "required": ["title", "author", "date", "content"] } """ html = clean_html(html) input_prompt = create_prompt(html, schema=schema) inputs = tokenizer.encode(input_prompt, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08) print(tokenizer.decode(outputs[0])) ``` ## AWS Sagemaker & Azure Marketplace & Google Cloud Platform Coming soon.