README.md

---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- ner
- named-entity-recognition
- token-classification
- knowledge-platform
- modernbert
- multilingual
- patents
- scientific-papers
- cross-domain
- english
- german
- generated_from_trainer
language:
- en
- de
metrics:
- precision
- recall
- f1
- accuracy
pipeline_tag: token-classification
model-index:
- name: knowledge-platform-ner
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    metrics:
    - type: f1
      value: 0.9063
      name: F1
    - type: precision
      value: 0.8951
      name: Precision
    - type: recall
      value: 0.9178
      name: Recall
    - type: accuracy
      value: 0.9811
      name: Accuracy
---

# Knowledge Platform NER

A cross-domain, multilingual Named Entity Recognition model built for the **Knowledge Platform** — a system that connects patents, scientific papers, news articles, and political documents across 13 data sources.

Fine-tuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on 256K+ multilingual documents spanning patents (USPTO, EPO), scientific papers (OpenAlex, arXiv), political documents (Bundestag, EU Parliament), and news.

## Key Results

| Metric | Score |
|---|---|
| **F1** | **90.6%** |
| Precision | 89.5% |
| Recall | 91.8% |
| Accuracy | 98.1% |

## Entity Types

The model recognizes **15 entity types** using BIO tagging (31 labels total):

| Tag | Entity Type | Example |
|---|---|---|
| `PER` | Person | *James Chen*, *Lisa Paus*, *Yann LeCun* |
| `ORG` | Organization | *Samsung Electronics*, *Bundestag*, *OpenAI* |
| `LOC` | Location | *Seoul*, *Brüssel*, *New York* |
| `ANIM` | Animal | *E. coli*, *SARS-CoV-2* |
| `BIO` | Biological | *CRISPR-Cas9*, *mRNA* |
| `CEL` | Celestial Body | *Mars*, *Jupiter* |
| `DIS` | Disease | *Alzheimer's*, *sickle cell disease* |
| `EVE` | Event | *COP28*, *World Economic Forum* |
| `FOOD` | Food | *glyphosate*, *insulin* |
| `INST` | Instrument | *LiDAR*, *mass spectrometer* |
| `MEDIA` | Media/Work | *Nature*, *The Lancet* |
| `MYTH` | Mythological | *Apollo* (program context) |
| `PLANT` | Plant | *Arabidopsis*, *cannabis sativa* |
| `TIME` | Time | *Q3 2025*, *fiscal year 2024* |
| `VEHI` | Vehicle | *Falcon 9*, *Boeing 787* |

## Use Cases

This model is designed for **knowledge graph construction** from heterogeneous document collections:

- **Patent Analysis**: Extract assignees, inventors, locations, and technologies from patent filings
- **Scientific Literature**: Identify authors, institutions, biological entities, and instruments from papers
- **Political Document Processing**: Extract politicians, parties, organizations from parliamentary debates (EN + DE)
- **News Processing**: Identify key entities across news articles for event tracking
- **Cross-Domain Knowledge Graphs**: Connect entities that appear across different document types and languages

### Works with the Knowledge Platform Embedding Model

This model is designed to work alongside [deepakint/knowledge-platform-embeddings](https://huggingface.co/deepakint/knowledge-platform-embeddings) — a SciNCL-based embedding model fine-tuned with contrastive learning on the same document corpus.

**Together they form a pipeline:**
1. **This NER model** extracts entities (the nodes of a knowledge graph)
2. **The embedding model** finds document connections (the edges of a knowledge graph)

## Quick Start

```python
from transformers import pipeline

ner = pipeline(
    "ner",
    model="deepakint/knowledge-platform-ner",
    aggregation_strategy="max"
)

# English patent text
text = "Samsung Electronics Co., Ltd. filed a patent at the USPTO in Washington, D.C."
entities = ner(text)

for entity in entities:
    print(f"  {entity['word']:40s} {entity['entity_group']:10s} {entity['score']:.3f}")
```

```
  Samsung Electronics Co., Ltd.            ORG        1.000
  USPTO                                    ORG        0.998
  Washington, D.C.                         LOC        0.999
```

```python
# German political text
text = "Lisa Paus sprach im Deutschen Bundestag in Berlin über die neue Regulierung."
entities = ner(text)

for entity in entities:
    print(f"  {entity['word']:40s} {entity['entity_group']:10s} {entity['score']:.3f}")
```

```
  Lisa Paus                                PER        1.000
  Deutschen Bundestag                      ORG        1.000
  Berlin                                   LOC        1.000
```

## Grouping Entities by Type

```python
from collections import defaultdict

text = """Apple Inc. CEO Tim Cook announced a new research lab in Palo Alto, 
California, partnering with Stanford University on CRISPR gene editing research."""

entities = ner(text)
grouped = defaultdict(list)
for ent in entities:
    grouped[ent["entity_group"]].append(ent["word"])

for label, names in sorted(grouped.items()):
    print(f"  {label:8s}: {names}")
```

```
  BIO     : ['CRISPR']
  LOC     : ['Palo Alto', 'California']
  ORG     : ['Apple Inc.', 'Stanford University']
  PER     : ['Tim Cook']
```

## Training Details

### Base Model

[answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) — a 149M parameter encoder model with:
- 8,192 token context length (vs. 512 for classic BERT)
- Rotary Position Embeddings (RoPE)
- Alternating full + sliding window attention
- Pre-trained on 2 trillion tokens of English text

### Training Data

~256,000 documents from 13 data sources across multiple domains and languages:

| Domain | Sources | Language |
|---|---|---|
| Patents | USPTO, EPO | EN, DE |
| Scientific Papers | OpenAlex, arXiv | EN |
| Political Documents | Bundestag, EU Parliament | DE, EN |
| News | Various | EN, DE |

### Hyperparameters

| Parameter | Value |
|---|---|
| Learning rate | 2e-05 |
| Batch size | 16 (x2 gradient accumulation = 32 effective) |
| Epochs | 3 |
| Optimizer | AdamW |
| LR scheduler | Cosine with 10% warmup |
| Seed | 42 |

### Training Progress

| Epoch | Training Loss | Validation Loss | Precision | Recall | F1 | Accuracy |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| 1 | 0.1276 | 0.0766 | 0.8595 | 0.8361 | 0.8476 | 0.9728 |
| 2 | 0.0927 | 0.0623 | 0.8659 | 0.8923 | 0.8789 | 0.9777 |
| 3 | 0.0422 | 0.0694 | 0.8707 | 0.8949 | 0.8827 | 0.9778 |

**Note:** The best checkpoint (epoch ~2, lowest validation loss 0.0606) was selected as the final model, achieving **90.6% F1**.

## Strengths and Limitations

### Strengths
- **Cross-domain**: Works on patents, papers, news, and political documents with a single model
- **Multilingual**: Handles both English and German text
- **Rich entity types**: 15 entity types covering people, organizations, locations, biological entities, diseases, instruments, and more
- **Fast**: ~5ms per document on CPU — suitable for processing millions of documents
- **Long context**: Inherits ModernBERT's 8,192 token context window

### Limitations
- **Conference/product names**: May fragment uncommon compound names (e.g., "NeurIPS" split into tokens) — use confidence thresholding (>0.5) to filter
- **Languages**: Optimized for English and German; other languages may work but are untested
- **Domain drift**: Performance is best on patent, scientific, political, and news text — may degrade on informal text (social media, chat)

## Recommended Post-Processing

For production use, apply a confidence threshold to filter low-quality predictions:

```python
# Filter entities with confidence > 0.5
entities = [e for e in ner(text) if e["score"] > 0.5]
```

## Framework Versions

- Transformers: 5.6.0
- PyTorch: 2.5.1+cu121
- Datasets: 4.8.4
- Tokenizers: 0.22.2

## Citation

```bibtex
@misc{knowledge-platform-ner-2026,
  title={Knowledge Platform NER: Cross-Domain Multilingual Named Entity Recognition},
  author={deepakint},
  year={2026},
  url={https://huggingface.co/deepakint/knowledge-platform-ner}
}
```

## Related Models

- **Embedding Model**: [deepakint/knowledge-platform-embeddings](https://huggingface.co/deepakint/knowledge-platform-embeddings) — Cross-domain semantic search and document matching