---
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
metrics:
- precision
- recall
- f1
widget:
- text: The Bengal tiger is the most common subspecies of tiger, constituting approximately
    80% of the entire tiger population, and is found in Bangladesh, Bhutan, Myanmar,
    Nepal, and India.
- text: In other countries, it is a non-commissioned rank (e.g. Spain, Italy, France,
    the Netherlands and the Indonesian Police ranks).
- text: The filling consists of fish, pork and bacon, and is seasoned with salt (unless
    the pork is already salted).
- text: This stood until August 20, 1993 when it was beaten by one 1 / 100th of a
    second by Colin Jackson of Great Britain in Stuttgart, Germany, a subsequent record
    that stood for 13 years.
- text: Ann Patchett ’s novel " Bel Canto ", was another creative influence that helped
    her manage a plentiful cast of characters.
pipeline_tag: token-classification
model-index:
- name: SpanMarker
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: Unknown
      type: unknown
      split: eval
    metrics:
    - type: f1
      value: 0.9130661114003124
      name: F1
    - type: precision
      value: 0.9148758606300855
      name: Precision
    - type: recall
      value: 0.9112635078969243
      name: Recall
---

# SpanMarker

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model 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:** 6 words
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **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                                                                 |
|:------|:-------------------------------------------------------------------------|
| ANIM  | "vertebrate", "moth", "G. firmus"                                        |
| BIO   | "Aspergillus", "Cladophora", "Zythiostroma"                              |
| CEL   | "pulsar", "celestial bodies", "neutron star"                             |
| DIS   | "social anxiety disorder", "insulin resistance", "Asperger syndrome"     |
| EVE   | "Spanish Civil War", "National Junior Angus Show", "French Revolution"   |
| FOOD  | "Neera", "Bellini ( cocktail )", "soju"                                  |
| INST  | "Apple II", "Encyclopaedia of Chess Openings", "Android"                 |
| LOC   | "Kīlauea", "Hungary", "Vienna"                                           |
| MEDIA | "CSI : Crime Scene Investigation", "Big Comic Spirits", "American Idol"  |
| MYTH  | "Priam", "Oźwiena", "Odysseus"                                           |
| ORG   | "San Francisco Giants", "Arm Holdings", "RTÉ One"                        |
| PER   | "Amelia Bence", "Tito Lusiardo", "James Cameron"                         |
| PLANT | "vernal squill", "Sarracenia purpurea", "Drosera rotundifolia"           |
| TIME  | "prehistory", "Age of Enlightenment", "annual paid holiday"              |
| VEHI  | "Short 360", "Ferrari 355 Challenge", "Solution F / Chretien Helicopter" |

## 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("Ann Patchett ’s novel \" Bel Canto \", was another creative influence that helped her manage a plentiful cast of characters.")
```

### 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       | 2   | 21.6493 | 237 |
| Entities per sentence | 0   | 1.5369  | 36  |

### Training Hyperparameters
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 1
- mixed_precision_training: Native AMP

### Training Results
| Epoch  | Step  | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.0576 | 1000  | 0.0142          | 0.8714               | 0.7729            | 0.8192        | 0.9698              |
| 0.1153 | 2000  | 0.0107          | 0.8316               | 0.8815            | 0.8558        | 0.9744              |
| 0.1729 | 3000  | 0.0092          | 0.8717               | 0.8797            | 0.8757        | 0.9780              |
| 0.2306 | 4000  | 0.0082          | 0.8811               | 0.8886            | 0.8848        | 0.9798              |
| 0.2882 | 5000  | 0.0084          | 0.8523               | 0.9163            | 0.8831        | 0.9790              |
| 0.3459 | 6000  | 0.0079          | 0.8700               | 0.9113            | 0.8902        | 0.9802              |
| 0.4035 | 7000  | 0.0070          | 0.9107               | 0.8859            | 0.8981        | 0.9822              |
| 0.4611 | 8000  | 0.0069          | 0.9259               | 0.8797            | 0.9022        | 0.9827              |
| 0.5188 | 9000  | 0.0067          | 0.9061               | 0.8965            | 0.9013        | 0.9829              |
| 0.5764 | 10000 | 0.0066          | 0.9034               | 0.8996            | 0.9015        | 0.9829              |
| 0.6341 | 11000 | 0.0064          | 0.9160               | 0.8996            | 0.9077        | 0.9839              |
| 0.6917 | 12000 | 0.0066          | 0.8952               | 0.9121            | 0.9036        | 0.9832              |
| 0.7494 | 13000 | 0.0062          | 0.9165               | 0.9009            | 0.9086        | 0.9841              |
| 0.8070 | 14000 | 0.0062          | 0.9010               | 0.9121            | 0.9065        | 0.9835              |
| 0.8647 | 15000 | 0.0062          | 0.9084               | 0.9127            | 0.9105        | 0.9842              |
| 0.9223 | 16000 | 0.0060          | 0.9151               | 0.9098            | 0.9125        | 0.9846              |
| 0.9799 | 17000 | 0.0060          | 0.9149               | 0.9113            | 0.9131        | 0.9848              |

### Framework Versions
- Python: 3.8.16
- SpanMarker: 1.5.0
- Transformers: 4.29.0.dev0
- PyTorch: 1.10.1
- Datasets: 2.15.0
- Tokenizers: 0.13.2

## 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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