---
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
widget:
- text: New Zealand Prime Minister Jim Bolger, emerging from coalition talks with
    the nationalist New Zealand First party on Friday afternoon, said National and
    NZ First would meet again on Sunday.
- text: A police spokesman said two youths believed to be supporters of President
    Nelson Mandela's African National Congress (ANC) had been killed when unknown
    gunmen opened fire at the rural settlement of Izingolweni on KwaZulu-Natal province's
    south coast on Thursday night.
- text: Japan's Economic Planning Agency has not changed its view that the economy
    is gradually recovering, despite relatively weak gross domestic product figures
    released on Tuesday, EPA Vice Minister Shimpei Nukaya told reporters on Friday.
- text: Cuttitta, who trainer George Coste said was certain to play on Saturday week,
    was named in a 21-man squad lacking only two of the team beaten 54-21 by England
    at Twickenham last month.
- text: Dong Jiong (China) beat Thomas Stuer-Lauridsen (Denmark) 15-10 15-6
pipeline_tag: token-classification
model-index:
- name: SpanMarker
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: Unknown
      type: conll2003
      split: test
    metrics:
    - type: f1
      value: 0.9209646189051223
      name: F1
    - type: precision
      value: 0.9156457822891144
      name: Precision
    - type: recall
      value: 0.9263456090651558
      name: Recall
---

# SpanMarker

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [conll2003](https://huggingface.co/datasets/conll2003) dataset that can be used for Named Entity Recognition.

## Model Details

### Model Description
- **Model Type:** SpanMarker
<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [conll2003](https://huggingface.co/datasets/conll2003)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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   | "BRUSSELS", "Britain", "Germany"                              |
| MISC  | "British", "EU-wide", "German"                                |
| ORG   | "European Union", "EU", "European Commission"                 |
| PER   | "Nikolaus van der Pas", "Peter Blackburn", "Werner Zwingmann" |

## Evaluation

### Metrics
| Label   | Precision | Recall | F1     |
|:--------|:----------|:-------|:-------|
| **all** | 0.9156    | 0.9263 | 0.9210 |
| LOC     | 0.9327    | 0.9394 | 0.9361 |
| MISC    | 0.7973    | 0.8462 | 0.8210 |
| ORG     | 0.8987    | 0.9133 | 0.9059 |
| PER     | 0.9706    | 0.9610 | 0.9658 |

## Uses

### Direct Use for Inference

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("supreethrao/instructNER_conll03_xl")
# Run inference
entities = model.predict("Dong Jiong (China) beat Thomas Stuer-Lauridsen (Denmark) 15-10 15-6")
```

### 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("supreethrao/instructNER_conll03_xl")

# 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("supreethrao/instructNER_conll03_xl-finetuned")
```
</details>

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## Training Details

### Training Set Metrics
| Training set          | Min | Median  | Max |
|:----------------------|:----|:--------|:----|
| Sentence length       | 1   | 14.5019 | 113 |
| Entities per sentence | 0   | 1.6736  | 20  |

### Training Hyperparameters
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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
- mixed_precision_training: Native AMP

### Framework Versions
- Python: 3.10.13
- SpanMarker: 1.5.0
- Transformers: 4.35.2
- PyTorch: 2.1.1
- 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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