README.md

---
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: Today in Zhongnanhai, General Secretary of the Communist Party of China, President
    of the country and honorary President of China's Red Cross, Zemin Jiang met with
    representatives of the 6th National Member Congress of China's Red Cross, and
    expressed warm greetings to the 20 million hardworking members on behalf of the
    Central Committee of the Chinese Communist Party and State Council.
- text: On April 20, 2017, MGM Television Studios, headed by Mark Burnett formed a
    partnership with McLane and Buss to produce and distribute new content across
    a number of media platforms.
- text: 'Postponed: East Fife v Clydebank, St Johnstone v'
- text: Prime contractor was Hughes Aircraft Company Electronics Division which developed
    the Tiamat with the assistance of the NACA.
- text: After graduating from Auburn University with a degree in Engineering in 1985,
    he went on to play inside linebacker for the Pittsburgh Steelers for four seasons.
pipeline_tag: token-classification
co2_eq_emissions:
  emissions: 248.1008753496152
  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: 1.766
  hardware_used: 1 x NVIDIA GeForce RTX 3090
base_model: bert-base-cased
model-index:
- name: SpanMarker with bert-base-cased 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: 0.7946954813359528
      name: F1
    - type: precision
      value: 0.7958325880879986
      name: Precision
    - type: recall
      value: 0.793561619404316
      name: Recall
---

# SpanMarker with bert-base-cased 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 [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, 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   | "Texas Chicken", "IAEA", "Church 's Chicken" |

## Evaluation

### Metrics
| Label   | Precision | Recall | F1     |
|:--------|:----------|:-------|:-------|
| **all** | 0.7958    | 0.7936 | 0.7947 |
| ORG     | 0.7958    | 0.7936 | 0.7947 |

## 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("Postponed: East Fife v Clydebank, St Johnstone v")
```

### 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   | 23.5706 | 263 |
| Entities per sentence | 0   | 0.7865  | 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 | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.7131 | 3000  | 0.0061          | 0.7978               | 0.7830            | 0.7904        | 0.9764              |
| 1.4262 | 6000  | 0.0059          | 0.8170               | 0.7843            | 0.8004        | 0.9774              |
| 2.1393 | 9000  | 0.0061          | 0.8221               | 0.7938            | 0.8077        | 0.9772              |
| 2.8524 | 12000 | 0.0062          | 0.8211               | 0.8003            | 0.8106        | 0.9780              |

### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.248 kg of CO2
- **Hours Used**: 1.766 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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