File size: 2,659 Bytes
0ce0188 aaa91c1 0ce0188 aaa91c1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
---
license: mit
datasets:
- gwlms/germeval2014
language:
- de
---
# 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:
| Model | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg.
| ---------------------- | ----- | ----- | ----- | ----- | ----- | -----
| GELECTRA Large (5e-05) | 89.99 | 89.55 | 89.60 | 89.34 | 89.68 | 89.63
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
```
# 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.")
``` |