---
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
datasets:
- tomaarsen/ner-orgs
metrics:
- precision
- recall
- f1
widget:
- text: The Fellowship of British Baptists and BMS World Mission brings together in
    ministry the churches that are members of the Baptist Union of Scotland, Wales,
    the Irish Baptist Networks, and the Baptist Union of Great Britain.
- text: The program is classified in the National Collegiate Athletic Association
    (NCAA) Division I Bowl Subdivision (FBS), and the team competes in the Big 12
    Conference.
- text: The Human Rights Foundation, condemned the assault, with HRF president Thor
    Halvorssen Mendoza claiming that "the PSUV approved of the attacks against opposition
    deputies at the National Assembly ".
- text: But senior Conservatives, such as Commons Health Committee chairperson Sarah
    Wollaston and education minister Anne Milton, backed calls for a free vote on
    the issue, while Labour MP Stella Creasy said she would table an amendment on
    the matter to the Domestic Violence Bill and said that over 150 parliamentarians
    had expressed support for the change, and Labour's shadow Attorney General Shami
    Chakrabarti called the issue a test fo r May's feminism.
- text: From 1991 to 1992, the Social Democratic Party and Social Democrats of Croatia
    were a part of the National Union government which was created by Franjo Tuđman
    during the first stages of the war.
pipeline_tag: token-classification
base_model: roberta-large
model-index:
- name: SpanMarker with roberta-large on FewNERD, CoNLL2003, and OntoNotes v5
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: FewNERD, CoNLL2003, and OntoNotes v5
      type: tomaarsen/ner-orgs
      split: test
    metrics:
    - type: f1
      value: 0.81019
      name: F1
    - type: precision
      value: 0.8238
      name: Precision
    - type: recall
      value: 0.7970
      name: Recall
---

# SpanMarker with roberta-large on FewNERD, CoNLL2003, and OntoNotes v5

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [FewNERD, CoNLL2003, and OntoNotes v5](https://huggingface.co/datasets/tomaarsen/ner-orgs) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [roberta-large](https://huggingface.co/roberta-large) as the underlying encoder.

## Model Details

### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [roberta-large](https://huggingface.co/roberta-large)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [FewNERD, CoNLL2003, and OntoNotes v5](https://huggingface.co/datasets/tomaarsen/ner-orgs)
- **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                                     |
|:------|:---------------------------------------------|
| ORG   | "IAEA", "Church 's Chicken", "Texas Chicken" |

## Evaluation

### Metrics
| Label   | Precision | Recall | F1     |
|:--------|:----------|:-------|:-------|
| ORG     | 0.8238    | 0.7970 | 0.81019|

## Uses

### Direct Use for Inference

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("nbroad/span-marker-roberta-large-orgs-v1")
# Run inference
entities = model.predict("The program is classified in the National Collegiate Athletic Association (NCAA) Division I Bowl Subdivision (FBS), and the team competes in the Big 12 Conference.")
```

### 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("nbroad/span-marker-roberta-large-orgs-v1")

# 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("nbroad/span-marker-roberta-large-orgs-v1-finetuned")
```
</details>

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## Training Details

### Training Set Metrics
| Training set          | Min | Median  | Max |
|:----------------------|:----|:--------|:----|
| Sentence length       | 1   | 23.5706 | 263 |
| Entities per sentence | 0   | 0.7865  | 39  |

### Training Hyperparameters
- learning_rate: 3e-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: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training Results
| Epoch  | Step  | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.1430 | 600   | 0.0085          | 0.7425               | 0.7383            | 0.7404        | 0.9726              |
| 0.2860 | 1200  | 0.0078          | 0.7503               | 0.7516            | 0.7510        | 0.9741              |
| 0.4290 | 1800  | 0.0077          | 0.6962               | 0.8107            | 0.7491        | 0.9718              |
| 0.5720 | 2400  | 0.0060          | 0.8074               | 0.7486            | 0.7769        | 0.9753              |
| 0.7150 | 3000  | 0.0057          | 0.8135               | 0.7717            | 0.7921        | 0.9770              |
| 0.8580 | 3600  | 0.0059          | 0.7997               | 0.7764            | 0.7879        | 0.9763              |
| 1.0010 | 4200  | 0.0057          | 0.7860               | 0.8051            | 0.7954        | 0.9771              |
| 1.1439 | 4800  | 0.0058          | 0.7907               | 0.7717            | 0.7811        | 0.9763              |
| 1.2869 | 5400  | 0.0058          | 0.8116               | 0.7803            | 0.7956        | 0.9774              |
| 1.4299 | 6000  | 0.0056          | 0.7918               | 0.7850            | 0.7884        | 0.9770              |
| 1.5729 | 6600  | 0.0056          | 0.8097               | 0.7837            | 0.7965        | 0.9769              |
| 1.7159 | 7200  | 0.0055          | 0.8113               | 0.7790            | 0.7948        | 0.9765              |
| 1.8589 | 7800  | 0.0052          | 0.8095               | 0.7970            | 0.8032        | 0.9782              |
| 2.0019 | 8400  | 0.0054          | 0.8244               | 0.7782            | 0.8006        | 0.9774              |
| 2.1449 | 9000  | 0.0053          | 0.8238               | 0.7970            | 0.8102        | 0.9782              |
| 2.2879 | 9600  | 0.0053          | 0.82                 | 0.7901            | 0.8048        | 0.9773              |
| 2.4309 | 10200 | 0.0053          | 0.8243               | 0.7936            | 0.8086        | 0.9785              |
| 2.5739 | 10800 | 0.0053          | 0.8159               | 0.7953            | 0.8055        | 0.9781              |
| 2.7169 | 11400 | 0.0053          | 0.8072               | 0.8034            | 0.8053        | 0.9784              |
| 2.8599 | 12000 | 0.0052          | 0.8111               | 0.8017            | 0.8064        | 0.9782              |

### Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.35.2
- PyTorch: 2.1.0a0+32f93b1
- 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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