---
language:
- de
license: mit
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
widget:
- text: Weitere Zulassungen folgten für Victoria und New South Wales 1975 und 1982
    am High Court of Australia.
- text: Ihr Name geht auf die Bethlehemskapelle in Prag zurück, die für die Böhmischen
    Brüder eine wichtige Rolle spielt.
- text: Sein Bundesliga-Debüt gab der Angreifer am 23.
- text: Er qualifizierte sich für die Teilnahme an den Olympischen Spielen 2008 in
    Peking und erreichte dort über 200 m die Viertelfinalrunde.
- text: Damit trat sie die Nachfolge des Sozialdemokraten Jens Stoltenberg an.
pipeline_tag: token-classification
base_model: numind/generic-entity_recognition_NER-multilingual-v1
model-index:
- name: SpanMarker with numind/generic-entity_recognition_NER-multilingual-v1 on wikiann
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: Unknown
      type: wikiann
      split: eval
    metrics:
    - type: f1
      value: 0.9069700043471961
      name: F1
    - type: precision
      value: 0.9069700043471961
      name: Precision
    - type: recall
      value: 0.9069700043471961
      name: Recall
---

# SpanMarker with numind/generic-entity_recognition_NER-multilingual-v1 on wikiann

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [wikiann](https://huggingface.co/datasets/wikiann) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [numind/generic-entity_recognition_NER-multilingual-v1](https://huggingface.co/numind/generic-entity_recognition_NER-multilingual-v1) as the underlying encoder.

## Model Details

### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [numind/generic-entity_recognition_NER-multilingual-v1](https://huggingface.co/numind/generic-entity_recognition_NER-multilingual-v1)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 9 words
- **Training Dataset:** [wikiann](https://huggingface.co/datasets/wikiann)
- **Language:** de
- **License:** mit

### Model Sources

- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)

### Model Labels
| Label | Examples                                                            |
|:------|:--------------------------------------------------------------------|
| LOC   | "Savoyer Voralpen", "Bagan", "Zechin"                               |
| ORG   | "NHL Entry Draft", "SKA Sankt Petersburg", "Minnesota Wild"         |
| PER   | "Antonina Wladimirowna Kriwoschapka", "Lou Salomé", "Jaan Kirsipuu" |

## Evaluation

### Metrics
| Label   | Precision | Recall | F1     |
|:--------|:----------|:-------|:-------|
| **all** | 0.9070    | 0.9070 | 0.9070 |
| LOC     | 0.9036    | 0.9298 | 0.9165 |
| ORG     | 0.8638    | 0.8446 | 0.8541 |
| PER     | 0.9507    | 0.9405 | 0.9455 |

## Uses

### Direct Use for Inference

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Run inference
entities = model.predict("Sein Bundesliga-Debüt gab der Angreifer am 23.")
```

### Downstream Use
You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

```python
from span_marker import SpanMarkerModel, Trainer

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")

# Specify a Dataset with "tokens" and "ner_tag" columns
dataset = load_dataset("conll2003") # For example CoNLL2003

# Initialize a Trainer using the pretrained model & dataset
trainer = Trainer(
    model=model,
    train_dataset=dataset["train"],
    eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("span_marker_model_id-finetuned")
```
</details>

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## Training Details

### Training Set Metrics
| Training set          | Min | Median | Max |
|:----------------------|:----|:-------|:----|
| Sentence length       | 1   | 9.7693 | 85  |
| Entities per sentence | 1   | 1.3821 | 20  |

### Training Hyperparameters
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training Results
| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 1.2658 | 200  | 0.0172          | 0.8842               | 0.8534            | 0.8686        | 0.9586              |
| 2.5316 | 400  | 0.0145          | 0.8977               | 0.8889            | 0.8933        | 0.9670              |
| 3.7975 | 600  | 0.0161          | 0.8962               | 0.9006            | 0.8984        | 0.9688              |
| 5.0633 | 800  | 0.0180          | 0.8982               | 0.8996            | 0.8989        | 0.9689              |
| 6.3291 | 1000 | 0.0201          | 0.9014               | 0.9008            | 0.9011        | 0.9694              |
| 7.5949 | 1200 | 0.0201          | 0.9010               | 0.9057            | 0.9033        | 0.9702              |
| 8.8608 | 1400 | 0.0217          | 0.9062               | 0.9036            | 0.9049        | 0.9702              |

### Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu118
- Datasets: 2.15.0
- Tokenizers: 0.15.0

## Citation

### BibTeX
```
@software{Aarsen_SpanMarker,
    author = {Aarsen, Tom},
    license = {Apache-2.0},
    title = {{SpanMarker for Named Entity Recognition}},
    url = {https://github.com/tomaarsen/SpanMarkerNER}
}
```

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