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---
base_model: roberta-base
datasets:
- conll2003
language:
- en
library_name: span-marker
license: apache-2.0
metrics:
- precision
- recall
- f1
pipeline_tag: token-classification
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
widget:
- text: '" The worst thing that could happen for financial markets is that if Clinton
    and Dole start to trade shots in the middle of the ring with one-upmanship, "
    said Hugh Johnson, chief investment officer at First Albany Corp. " That''s when
    Wall Street will need to worry . "'
- text: Poland revived diplomatic ties at ambassadorial level with Yugoslavia in April
    but economic links are almost moribund, despite the end of a three-year U.N. trade
    embargo imposed to punish Belgrade for its support of Bosnian Serbs.
- text: '" We believe that the Israeli settlement policy in the occupied areas is
    an obstacle to the establishment of peace, " German Foreign Ministry spokesman
    Martin Erdmann said.'
- text: U.S. Agriculture Department officials said Friday that Mexican avocados--which
    are restricted from entering the continental United States--will not likely be
    entering U.S. markets any time soon, even if the controversial ban were lifted
    today.
- text: 3. Tristan Hoffman (Netherlands) TVM same time
model-index:
- name: SpanMarker with roberta-base on conll2003
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: Unknown
      type: conll2003
      split: test
    metrics:
    - type: f1
      value: 0.9022464022464022
      name: F1
    - type: precision
      value: 0.8943980514961726
      name: Precision
    - type: recall
      value: 0.9102337110481586
      name: Recall
---

# SpanMarker with roberta-base on conll2003

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. This SpanMarker model uses [roberta-base](https://huggingface.co/roberta-base) as the underlying encoder.

## Model Details

### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [roberta-base](https://huggingface.co/roberta-base)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 6 words
- **Training Dataset:** [conll2003](https://huggingface.co/datasets/conll2003)
- **Language:** en
- **License:** apache-2.0

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

## Evaluation

### Metrics
| Label   | Precision | Recall | F1     |
|:--------|:----------|:-------|:-------|
| **all** | 0.8944    | 0.9102 | 0.9022 |
| LOC     | 0.9220    | 0.9215 | 0.9217 |
| MISC    | 0.7332    | 0.7949 | 0.7628 |
| ORG     | 0.8764    | 0.8964 | 0.8863 |
| PER     | 0.9605    | 0.9629 | 0.9617 |

## 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("3. Tristan Hoffman (Netherlands) TVM same time")
```

### 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       | 1   | 14.5019 | 113 |
| Entities per sentence | 0   | 1.6736  | 20  |

### Training Hyperparameters
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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 |
|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.2775 | 500  | 0.0282          | 0.9105               | 0.8355            | 0.8714        | 0.9670              |
| 0.5549 | 1000 | 0.0166          | 0.9215               | 0.9205            | 0.9210        | 0.9824              |
| 0.8324 | 1500 | 0.0151          | 0.9247               | 0.9346            | 0.9296        | 0.9853              |

### Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## 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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