---
language:
- en
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: Hallacas are also commonly consumed in eastern Cuba parts of Colombia, Ecuador,
    Aruba, and Curaçao.
- text: The co-production of Yvon Michel's GYM and Jean Bédard's Interbox promotions
    and televised via HBO, has trumped a proposed HBO -televised rematch between Jean
    Pascal and RING and WBC 175-pound champion Chad Dawson that was slated for the
    same date at Bell Centre in Montreal.
- text: The synoptic conditions see a low over southern Norway, bringing warm south
    and southwesterly flows of air up from the inner continental areas of Russia and
    Belarus.
- text: The RCIS recommended amongst other things that the Australian Security Intelligence
    Organisation (ASIO) areas of investigation be widened to include terrorism.
- text: The large network had multiple campuses in Minnesota, Wisconsin, and South
    Dakota.
pipeline_tag: token-classification
co2_eq_emissions:
  emissions: 532.6472478623315
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 3.696
  hardware_used: 1 x NVIDIA GeForce RTX 3090
base_model: bert-base-cased
model-index:
- name: SpanMarker with bert-base-cased on FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD
      type: tomaarsen/ner-orgs
      split: test
    metrics:
    - type: f1
      value: 0.8311343653918766
      name: F1
    - type: precision
      value: 0.8334090564894745
      name: Precision
    - type: recall
      value: 0.8288720574945131
      name: Recall
---

# SpanMarker with bert-base-cased on FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD

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

## Model Details

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

## Evaluation

### Metrics
| Label   | Precision | Recall | F1     |
|:--------|:----------|:-------|:-------|
| **all** | 0.8334    | 0.8289 | 0.8311 |
| ORG     | 0.8334    | 0.8289 | 0.8311 |

## Uses

### Direct Use for Inference

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-orgs")
# Run inference
entities = model.predict("The large network had multiple campuses in Minnesota, Wisconsin, and South Dakota.")
```

### 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("tomaarsen/span-marker-bert-base-orgs")

# 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("tomaarsen/span-marker-bert-base-orgs-finetuned")
```
</details>

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## Training Details

### Training Set Metrics
| Training set          | Min | Median  | Max |
|:----------------------|:----|:--------|:----|
| Sentence length       | 1   | 22.1911 | 267 |
| Entities per sentence | 0   | 0.8144  | 39  |

### 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 |
|:------:|:-----:|:---------------:|
| 0.3273 | 3000  | 0.0052          |
| 0.6546 | 6000  | 0.0047          |
| 0.9819 | 9000  | 0.0045          |
| 1.3092 | 12000 | 0.0047          |
| 1.6365 | 15000 | 0.0045          |
| 1.9638 | 18000 | 0.0046          |
| 2.2911 | 21000 | 0.0054          |
| 2.6184 | 24000 | 0.0053          |
| 2.9457 | 27000 | 0.0052          |

### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.533 kg of CO2
- **Hours Used**: 3.696 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB

### Framework Versions
- Python: 3.9.16
- SpanMarker: 1.5.1.dev
- Transformers: 4.30.0
- PyTorch: 2.0.1+cu118
- Datasets: 2.14.0
- Tokenizers: 0.13.3

## 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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