---
language: en
license: cc-by-sa-4.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
base_model: FacebookAI/xlm-roberta-base
datasets:
- norne
metrics:
- precision
- recall
- f1
widget:
- text: Av Boethius hand förelåg De institutione arithmetica (" Om aritmetikens grunder
    ") i två böcker.
- text: Hans hovedmotstander var lederen for opposisjonspartiet Movement for Democratic
    Change, Morgan Tsvangirai.
- text: Roddarn blir proffs efter OS.
- text: Han blev dog diskvalificeret for at have trådt på banelinjen, og bronzemedaljen
    gik i stedet til landsmanden Walter Dix.
- text: Stillingen var på dette tidspunkt 1-1, men Almunias redning banede vejen for
    et sejrsmål af danske Nicklas Bendtner.
pipeline_tag: token-classification
model-index:
- name: SpanMarker with FacebookAI/xlm-roberta-base on norne
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: norne
      type: norne
      split: test
    metrics:
    - type: f1
      value: 0.9181825779313034
      name: F1
    - type: precision
      value: 0.9217689611454993
      name: Precision
    - type: recall
      value: 0.9146239940801036
      name: Recall
---

# SpanMarker with xlm-roberta-base
Trained on various nordic lang. datasets: see https://huggingface.co/datasets/tollefj/nordic-ner

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [norne](https://huggingface.co/datasets/norne) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) as the underlying encoder.

## Model Details

### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [norne](https://huggingface.co/datasets/norne)
- **Language:** en
- **License:** cc-by-sa-4.0

### 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   | "Gran", "Leicestershire", "Den tyske antarktisekspedisjonen" |
| MISC  | "socialdemokratiske", "nationalist", "Living Legend"         |
| ORG   | "Stabæk", "Samlaget", "Marillion"                            |
| PER   | "Fish", "Dmitrij Medvedev", "Guru Ardjan Dev"                |

## Evaluation

### Metrics
| Label   | Precision | Recall | F1     |
|:--------|:----------|:-------|:-------|
| **all** | 0.9218    | 0.9146 | 0.9182 |
| LOC     | 0.9284    | 0.9433 | 0.9358 |
| MISC    | 0.6515    | 0.6047 | 0.6272 |
| ORG     | 0.8951    | 0.8547 | 0.8745 |
| PER     | 0.9513    | 0.9526 | 0.9520 |

## 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("Roddarn blir proffs efter OS.")
```

### 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   | 12.8175 | 331 |
| Entities per sentence | 0   | 1.0055  | 54  |

### Training Hyperparameters
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training Results
| Epoch  | Step  | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.5711 | 3000  | 0.0146          | 0.8650               | 0.8725            | 0.8687        | 0.9722              |
| 1.1422 | 6000  | 0.0123          | 0.8994               | 0.8920            | 0.8957        | 0.9778              |
| 1.7133 | 9000  | 0.0101          | 0.9184               | 0.8984            | 0.9083        | 0.9805              |
| 2.2844 | 12000 | 0.0101          | 0.9198               | 0.9110            | 0.9154        | 0.9818              |
| 2.8555 | 15000 | 0.0089          | 0.9245               | 0.9150            | 0.9197        | 0.9830              |

### Framework Versions
- Python: 3.12.2
- SpanMarker: 1.5.0
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.18.0
- Tokenizers: 0.15.2

## 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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