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---
library_name: transformers
base_model: meta-llama/Llama-3.1-8B-Instruct
---
<p align="center">
<img alt="Schematron" src="https://huggingface.co/inference-net/Schematron-3B/resolve/main/Banner.png">
</p>
<p align="center">
<a href="https://docs.inference.net/use-cases/json-extraction"><strong>Documentation</strong></a> ·
<a href="https://inference.net/models/schematron-8b"><strong>Serverless API</strong></a> ·
<a href="https://inference.net/blog/Schematron"><strong>Announcement blog</strong></a>
</p>
<br>
## Model Overview
Welcome to the Schematron series, [Inference.net's](https://inference.net/) long‑context extraction models specialized in converting noisy HTML into clean, typed JSON that conforms to your custom schema. The Schematron series was purpose‑trained for web scraping, data ingestion, and transforming arbitrary pages into structured records.
We're releasing these models in two different sizes:
- **Schematron‑8B** — marginal quality lift on harder/longer pages
- **Schematron‑3B** — recommended default; near‑parity quality at ~50% cost of Schematron-8B
> [!NOTE]
> This model card is dedicated to the smaller `Schematron-8B` model. Check out [`Schematron-3B`](https://huggingface.co/inference-net/Schematron-3B) for the smaller model.
## I/O at a glance
- **Input**: Cleaned HTML + JSON Schema (can be extracted from typed model like Pydantic/Zod)
- **Output**: Strictly valid JSON conforming to the provided schema (no narration)
> [!NOTE]
> The JSON Schema passed as input needs to conform to the [schema.org](https://json-schema.org/draft-07/schema) schema.
## Highlights
- **Schema-first extraction**: 100% schema‑conformant JSON outputs
- **Long context**: Robust to lengthy, noisy HTML (up to 128K tokens)
- **Variants**: 3B (default, most cost‑efficient) · 8B (marginal quality lift at ~2× cost)
## Model Details
- **Family**: Schematron (3B and 8B)
- **Context window**: Up to 128K tokens
- **Input**: Cleaned or raw HTML and a JSON Schema
- **Output**: Strict JSON that conforms to the provided schema
## Benchmarks
### HTML-to-JSON Extraction Quality
We evaluated extraction quality using Gemini 2.5 Pro as a judge, scoring extractions from 1-5 where 5 represents perfect extraction.
| Model | LLM-as-Judge Score |
|-------|-------------------|
| GPT-4.1 | 4.74 |
| **Schematron-8B** | **4.64** |
| **Schematron-3B** | **4.41** |
| Gemini-3B-Base | 2.24 |
### Web-Augmented Factuality on SimpleQA
We evaluated Schematron's real-world impact on LLM factuality using SimpleQA.
**Test Pipeline:**
1. **Query Generation**: Primary LLM (GPT-5 Nano or GPT-4.1) generates search queries and defines extraction schema
2. **Web Search**: Search provider (SERP or Exa) retrieves relevant pages
3. **Structured Extraction**: Schematron extracts JSON data from retrieved pages using the schema
4. **Answer Synthesis**: Primary LLM produces final answer from structured data
![image/png](https://cdn-uploads.huggingface.co/production/uploads/6626a246891c75742bd19aaf/mU_01IPsf0FvkXYNYstRZ.png)
**Key findings:**
- Web search paired with JSON extraction improves factuality: Adding Schematron with web retrieval improves GPT-5 Nano's accuracy from 8.54% to 82.87%—nearly a 10x improvement
- Search provider matters: Exa (82.9%) significantly outperforms SERP (64.2%) for factual retrieval, while also being more cost-effective
- Structured extraction beats raw HTML: Processing raw HTML would require 100k+ tokens for 10 searches; Schematron's JSON extraction reduces this by orders of magnitude
- Small specialized models win: Schematron-8B (82.87%) outperforms the much larger Gemini 2.5 Flash (80.61%) on this task, showing that fine-tuning for well-defined tasks beats general purpose models
- Performance scales with model quality: When paired with GPT-4.1, Schematron achieves 85.58% accuracy, showing the approach benefits from stronger base models
## Minimal Quickstart
Use these local snippets to prepare HTML and compose a schema‑guided prompt. The model returns strictly valid JSON; validate it against your schema downstream.
```python
from lxml.html.clean import Cleaner
import lxml.html as LH
HTML_CLEANER = Cleaner(
scripts=True,
javascript=True,
style=True,
inline_style=True,
safe_attrs_only=False,
)
def strip_noise(html: str) -> str:
"""Remove scripts, styles, and JavaScript from HTML using lxml.
"""
if not html or not html.strip():
return ""
try:
doc = LH.fromstring(html)
cleaned = HTML_CLEANER.clean_html(doc)
return LH.tostring(cleaned, encoding="unicode")
except Exception:
return ""
```
Compose messages with your schema and cleaned HTML:
```python
def construct_messages(schema: str, html: str):
"""Construct messages for a schema‑guided extraction request."""
response_prompt = {
"prompt_part_one": (
"You are going to be given a JSON schema following the standardized JSON "
"Schema format. You are going to be given a HTML page and you are going "
"to apply the schema to the HTML page however you see it as applicable "
"and return the results in a JSON object. The schema is as follows:"
),
"prompt_part_two": "Here is the HTML page:",
"prompt_part_three": "MAKE SURE ITS VALID JSON.",
}
user_prompt = (
response_prompt['prompt_part_one']
+ "\n\n" + schema + "\n\n"
+ response_prompt['prompt_part_two']
+ "\n\n" + html + "\n\n"
+ response_prompt['prompt_part_three']
)
return [
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": user_prompt},
]
```
> [!NOTE]
> In the [serverless API](https://inference.net/models/schematron-3b) there's no need to pass anything but the HTML. We handle the prompt formatting for you.
## Recommendations
- Temperature 0 and JSON mode for deterministic, parseable output
- Validate responses against your schema (e.g., Pydantic or Zod)
- Pre‑clean HTML (remove scripts/styles) when possible; avoid over‑aggressive removal
- Using lxml to clean the HTML is not required, but is recommended as it matches the training data.
## Limitations
- Static HTML only; render client‑side content upstream
- Very large pages may require truncation
- Ambiguous fields depend on schema clarity; be explicit in field descriptions
## Safety and Responsible Use
- Extracted data may include personal or sensitive information present in the page—handle and store responsibly
- Respect site terms, robots.txt, and applicable laws
- Use downstream validation and guardrails for compliance
## License
See license in the metadata above.
## Support
- Docs: https://docs.inference.net/use-cases/json-extraction
- Email: support@inference.net

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{{- bos_token }}
{%- if custom_tools is defined %}
{%- set tools = custom_tools %}
{%- endif %}
{%- if not tools_in_user_message is defined %}
{%- set tools_in_user_message = true %}
{%- endif %}
{%- if not date_string is defined %}
{%- set date_string = "26 Jul 2024" %}
{%- endif %}
{%- if not tools is defined %}
{%- set tools = none %}
{%- endif %}
{#- This block extracts the system message, so we can slot it into the right place. #}
{%- if messages[0]['role'] == 'system' %}
{%- set system_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{%- set system_message = "" %}
{%- endif %}
{#- System message + builtin tools #}
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
{%- if builtin_tools is defined or tools is not none %}
{{- "Environment: ipython\n" }}
{%- endif %}
{%- if builtin_tools is defined %}
{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
{%- endif %}
{{- "Cutting Knowledge Date: December 2023\n" }}
{{- "Today Date: " + date_string + "\n\n" }}
{%- if tools is not none and not tools_in_user_message %}
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{%- endif %}
{{- system_message }}
{{- "<|eot_id|>" }}
{#- Custom tools are passed in a user message with some extra guidance #}
{%- if tools_in_user_message and not tools is none %}
{#- Extract the first user message so we can plug it in here #}
{%- if messages | length != 0 %}
{%- set first_user_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
{%- endif %}
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
{{- "Given the following functions, please respond with a JSON for a function call " }}
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{{- first_user_message + "<|eot_id|>"}}
{%- endif %}
{%- for message in messages %}
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
{%- elif 'tool_calls' in message %}
{%- if not message.tool_calls|length == 1 %}
{{- raise_exception("This model only supports single tool-calls at once!") }}
{%- endif %}
{%- set tool_call = message.tool_calls[0].function %}
{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- "<|python_tag|>" + tool_call.name + ".call(" }}
{%- for arg_name, arg_val in tool_call.arguments | items %}
{{- arg_name + '="' + arg_val + '"' }}
{%- if not loop.last %}
{{- ", " }}
{%- endif %}
{%- endfor %}
{{- ")" }}
{%- else %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- '{"name": "' + tool_call.name + '", ' }}
{{- '"parameters": ' }}
{{- tool_call.arguments | tojson }}
{{- "}" }}
{%- endif %}
{%- if builtin_tools is defined %}
{#- This means we're in ipython mode #}
{{- "<|eom_id|>" }}
{%- else %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- elif message.role == "tool" or message.role == "ipython" %}
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
{%- if message.content is mapping or message.content is iterable %}
{{- message.content | tojson }}
{%- else %}
{{- message.content }}
{%- endif %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
{%- endif %}

35
config.json Normal file
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