metadata
language:
- de
library_name: tflite
tags:
- named-entity-recognition
- ner
- german
- tflite
- on-device
- mobile
- android
- ios
datasets:
- GermanEval/germeval_14
base_model: deepset/gelectra-large
pipeline_tag: token-classification
license: mit
MobAnon NER Model
German Named Entity Recognition model for the MobAnon document anonymization app. Fine-tuned from deepset/gelectra-large on GermEval14 for on-device inference.
Model Details
| Property | Value |
|---|---|
| Base model | deepset/gelectra-large |
| Training data | GermEval14 (German NER) |
| Format | TensorFlow Lite (float16 quantized) |
| Size | ~638 MB |
| Test F1 | ~87-89% |
| Max sequence length | 128 tokens |
Entity Types
The model detects four semantic entity types using BIO tagging:
| Entity | Examples |
|---|---|
| PERSON | Max Mustermann, Dr. Schmidt |
| ORGANIZATION | Deutsche Bank, Bundesgerichtshof |
| LOCATION | Frankfurt, Deutschland, Berliner Str. |
| MISC | Events, dates, other named entities |
MobAnon supplements these with regex-based detection for structured entities (email, phone, IBAN, identifiers).
Usage
This model is downloaded automatically by the MobAnon app on first use. No manual setup required.
Direct download
# Via huggingface-cli
huggingface-cli download PaulCamacho/mobanon-models deepseek.tflite
# Via URL
wget https://huggingface.co/PaulCamacho/mobanon-models/resolve/main/deepseek.tflite
Input/Output Specification
| Tensor | Shape | Type | Description |
|---|---|---|---|
input_ids |
[1, 128] | int32 | Tokenized input IDs |
attention_mask |
[1, 128] | int32 | Attention mask |
logits |
[1, 128, 9] | float32 | Per-token logits for 9 BIO labels |
Labels
| Index | Label | Entity |
|---|---|---|
| 0 | O | Outside |
| 1 | B-PER | Begin Person |
| 2 | I-PER | Inside Person |
| 3 | B-ORG | Begin Organization |
| 4 | I-ORG | Inside Organization |
| 5 | B-LOC | Begin Location |
| 6 | I-LOC | Inside Location |
| 7 | B-MISC | Begin Miscellaneous |
| 8 | I-MISC | Inside Miscellaneous |
Training
cd base_model
python train_ner.py --epochs 3 --batch-size 16 --fp16
python export_to_onnx.py --static-shapes
python convert_to_tflite.py --quantize float16
See the base_model README for the full training and conversion pipeline.
License
MIT