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