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---
license: mit
library_name: span-marker
base_model: deepset/gelectra-large
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
pipeline_tag: token-classification
widget:
- text: "Jürgen Schmidhuber studierte ab 1983 Informatik und Mathematik an der TU München ."
example_title: "Wikipedia"
datasets:
- gwlms/germeval2014
language:
- de
model-index:
- name: SpanMarker with GELECTRA Large on GermEval 2014 NER Dataset by Stefan Schweter (@stefan-it)
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
type: gwlms/germeval2014
name: GermEval 2014
split: test
revision: f3647c56803ce67c08ee8d15f4611054c377b226
metrics:
- type: f1
value: 0.8908
name: F1
- type: precision
value: 0.8901
name: Precision
- type: recall
value: 0.8916
name: Recall
metrics:
- f1
- recall
- precision
---
# SpanMarker for GermEval 2014 NER
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that
was fine-tuned on the [GermEval 2014 NER Dataset](https://sites.google.com/site/germeval2014ner/home).
The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following
properties: The data was sampled from German Wikipedia and News Corpora as a collection of citations. The dataset
covers over 31,000 sentences corresponding to over 590,000 tokens. The NER annotation uses the NoSta-D guidelines,
which extend the Tübingen Treebank guidelines, using four main NER categories with sub-structure, and annotating
embeddings among NEs such as `[ORG FC Kickers [LOC Darmstadt]]`.
12 classes of Named Entites are annotated and must be recognized: four main classes `PER`son, `LOC`ation, `ORG`anisation,
and `OTH`er and their subclasses by introducing two fine-grained labels: `-deriv` marks derivations from NEs such as
"englisch" (“English”), and `-part` marks compounds including a NE as a subsequence deutschlandweit (“Germany-wide”).
# Fine-Tuning
We use the same hyper-parameters as used in the
["German's Next Language Model"](https://aclanthology.org/2020.coling-main.598/) paper using the released
[GELECTRA Large](https://huggingface.co/deepset/gelectra-large) model as backbone.
Evaluation is performed with SpanMarkers internal evaluation code that uses `seqeval`. Additionally we use
the official GermEval 2014 Evaluation Script for double-checking the results. A backup of the `nereval.py` script
can be found [here](https://github.com/bplank/DaNplus/blob/master/scripts/nereval.perl).
We fine-tune 5 models and upload the model with best F1-Score on development set. Results on development set are
in brackets:
| Model | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg.
| ---------------------- | --------------- | --------------- | --------------- | --------------- | --------------- | ---------------
| GELECTRA Large (5e-05) | (89.99) / 89.08 | (89.55) / 89.23 | (89.60) / 89.10 | (89.34) / 89.02 | (89.68) / 88.80 | (89.63) / 89.05
The best model achieves a final test score of 89.08%:
```bash
1. Strict, Combined Evaluation (official):
Accuracy: 99.26%;
Precision: 89.01%;
Recall: 89.16%;
FB1: 89.08
```
Scripts for [training](trainer.py) and [evaluation](evaluator.py) are also available.
# Usage
The fine-tuned model can be used like:
```python
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("stefan-it/span-marker-gelectra-large-germeval14")
# Run inference
entities = model.predict("Jürgen Schmidhuber studierte ab 1983 Informatik und Mathematik an der TU München .")
``` |