
This model is a fine-tuned version of Leonard Konle's [fiction-gbert](https://huggingface.co/lkonle/fiction-gbert-large).

It was fine-tuned for ten epochs on the [Deutscher Roman Korpus (DROC)](https://gitlab2.informatik.uni-wuerzburg.de/kallimachos/DROC-Release/-/tree/master/droc) on literary character detection using a standard token-classification head.
However, in deviation from most other models, this model detects named entities and nouns (matching both "Harry" and "Zauberer") referencing a character.

The model achieves a 92.12 / 89.98 % F1 score on the semi-official DROC validation and test sets.

The code to reproduce the dataset and training can be accessed via [Github](https://github.com/LennartKeller/np-ner-lb)

Additional hyperparameters are:

* Num Epochs: 10
* Batch-Size: 8
* Optimizer: AdamW
* Learning rate: 2e-05
* Weight-Decay: 0.1
* Scheduler: Linear Warmup for the first 10 % of the training, with a linear decay for the remainder.
* Precision: 32bit
* Training-Framework: [Trident](https://github.com/fdschmidt93/trident/tree/main)



`ID2Label-Map`:

```json
{
0: "O",
1: "B-PER",
2: "I-Per"
}
```
{"language": ["de"], "license": "mit", "library_name": "transformers", "tags": ["ner", "droc", "character-detection"], "metrics": ["accuracy"], "widget": [{"text": "Der Zauberer schwieg w\u00e4hrend Frodo den Ring betrachtete. Der Hobbit sah Gandalf an."}], "inference": {"parameters": {"aggregation_strategy": "first"}}, "pipeline_tag": "token-classification"}
transformers safetensors bert token-classification ner droc character-detection de license:mit autotrain_compatible endpoints_compatible region:us