Llama-3.1-8B-Poster-Extraction/README.md

---
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
  - poster-extraction
  - scientific-posters
  - machine-actionable
  - FAIR-data
  - biomedical
  - document-understanding
  - structured-extraction
  - datacite
  - text-generation
  - llama3
language:
  - en
pipeline_tag: text-generation
library_name: transformers
---

# Llama-3.1-8B-Poster-Extraction

## Model Description

This model powers the extraction pipeline for [**posters.science**](https://posters.science), a platform for making scientific conference posters Findable, Accessible, Interoperable, and Reusable (FAIR).

The model converts raw poster text into structured JSON metadata conforming to the [**poster-json-schema**](https://github.com/fairdataihub/poster-json-schema)—a DataCite-based schema extended for poster-specific metadata including conference information, content sections, and figure/table captions.

Developed by the [**FAIR Data Innovations Hub**](https://fairdataihub.org/) at the California Medical Innovations Institute (CalMI²).

## poster2json Library

This model is the core of the **poster2json** Python library:

| Resource | Link |
|----------|------|
| **PyPI** | [poster2json](https://pypi.org/project/poster2json/) |
| **Documentation** | [fairdataihub.github.io/poster2json](https://fairdataihub.github.io/poster2json/) |
| **GitHub** | [fairdataihub/poster2json](https://github.com/fairdataihub/poster2json) |
| **API Repository** | [fairdataihub/posters-science-extraction-api](https://github.com/fairdataihub/posters-science-extraction-api) |
| **Platform** | [posters.science](https://posters.science) |

### Quick Install

```bash
pip install poster2json
```

### Python Usage

```python
from poster2json import extract_poster

result = extract_poster("path/to/poster.pdf")
print(result["titles"][0]["title"])
print(result["creators"])
```

## Output Schema

Output conforms to the [poster-json-schema](https://github.com/fairdataihub/poster-json-schema), based on [DataCite Metadata Schema](https://datacite.org/) with poster-specific extensions:

```json
{
  "$schema": "https://posters.science/schema/v0.1/poster_schema.json",
  "creators": [
    {
      "name": "Garcia, Sofia",
      "givenName": "Sofia",
      "familyName": "Garcia",
      "nameType": "Personal",
      "affiliation": ["University of California, San Diego"]
    }
  ],
  "titles": [
    { "title": "Machine Learning Approaches to Diabetic Retinopathy Detection" }
  ],
  "publicationYear": 2025,
  "subjects": [
    { "subject": "Machine Learning" },
    { "subject": "Diabetic Retinopathy" }
  ],
  "descriptions": [
    {
      "description": "This poster presents machine learning methods for automated diabetic retinopathy screening...",
      "descriptionType": "Abstract"
    }
  ],
  "conference": {
    "conferenceName": "AMIA 2025 Annual Symposium",
    "conferenceLocation": "San Francisco, CA"
  },
  "content": {
    "sections": [
      { "sectionTitle": "Introduction", "sectionContent": "..." },
      { "sectionTitle": "Methods", "sectionContent": "..." },
      { "sectionTitle": "Results", "sectionContent": "..." },
      { "sectionTitle": "Conclusions", "sectionContent": "..." }
    ]
  },
  "imageCaptions": [
    { "caption": "Figure 1. ROC curves showing model performance across datasets" }
  ],
  "tableCaptions": [
    { "caption": "Table 1. Summary of demographic characteristics" }
  ],
  "rightsList": [
    { "rights": "Creative Commons Attribution 4.0 International" }
  ],
  "formats": ["PDF"]
}
```

### Key Schema Fields (DataCite-based)

| Field | Description |
|-------|-------------|
| `creators` | Authors with name, affiliation, ORCID identifiers |
| `titles` | Main title and alternative/translated titles |
| `subjects` | Keywords and classification codes (MeSH, LCSH) |
| `descriptions` | Abstract, methods, technical information |
| `conference` | Conference name, location, dates, URI |
| `content.sections` | Extracted poster sections with titles and content |
| `imageCaptions` | Figure captions extracted from the poster |
| `tableCaptions` | Table captions extracted from the poster |
| `fundingReferences` | Grant information (funder, award number) |
| `rightsList` | License information (CC-BY, etc.) |
| `relatedIdentifiers` | DOIs, URLs to related resources |

## Model Specifications

| Attribute | Value |
|-----------|-------|
| Base Model | meta-llama/Llama-3.1-8B-Instruct |
| Parameters | 8 Billion |
| Context Length | 128K tokens |
| Architecture | LLaMA 3.1 |
| Precision | bfloat16 |
| License | Llama 3.1 Community License |

## Performance

Validated on 10 manually annotated scientific posters:

| Metric | Score | Threshold |
|--------|-------|-----------|
| Word Capture | 0.96 | ≥0.75 |
| ROUGE-L | 0.89 | ≥0.75 |
| Number Capture | 0.93 | ≥0.75 |
| Field Proportion | 0.99 | 0.50–2.00 |

**Pass Rate**: 10/10 (100%)

## Direct Usage (Transformers)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "fairdataihub/Llama-3.1-8B-Poster-Extraction"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto"
)

prompt = """Extract structured metadata from the following scientific poster.
Return valid JSON conforming to the poster-json-schema with fields:
creators, titles, publicationYear, subjects, descriptions, conference, content, imageCaptions, tableCaptions.

Poster Content:
[Your poster text here]
"""

messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=4096, temperature=0.1)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

## System Requirements

- **GPU**: NVIDIA CUDA-capable, ≥16GB VRAM (RTX 4090 recommended)
- **RAM**: ≥32GB
- Supports 8-bit quantization for memory-constrained environments
- Compatible with vLLM and other inference optimization frameworks

## Citation

```bibtex
@software{poster2json2026,
  title = {poster2json: Scientific Poster to JSON Metadata Extraction},
  author = {O'Neill, James and Soundarajan, Sanjay and Portillo, Dorian and Patel, Bhavesh},
  year = {2026},
  url = {https://github.com/fairdataihub/poster2json},
  doi = {10.5281/zenodo.18320010}
}
```

## License

This model is released under the [Llama 3.1 Community License](https://ai.meta.com/llama/license/).

## Acknowledgments

- [FAIR Data Innovations Hub](https://fairdataihub.org/) at California Medical Innovations Institute (CalMI²)
- [posters.science](https://posters.science) platform
- Meta AI for the Llama 3.1 base model
- HuggingFace for model hosting infrastructure
- Funded by The Navigation Fund ([10.71707/rk36-9x79](https://doi.org/10.71707/rk36-9x79)) — "Poster Sharing and Discovery Made Easy"