---
language:
- es
license: cc-by-4.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
widget:
- text: George Washington estuvo en Washington.
pipeline_tag: token-classification
base_model: PlanTL-GOB-ES/roberta-base-bne
model-index:
- name: SpanMarker with PlanTL-GOB-ES/roberta-base-bne on conll2002
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: conll2002
      type: conll2002
      split: eval
    metrics:
    - type: f1
      value: 0.871172868582195
      name: F1
    - type: precision
      value: 0.888328530259366
      name: Precision
    - type: recall
      value: 0.8546672828096118
      name: Recall
---

# SpanMarker with PlanTL-GOB-ES/roberta-base-bne on conll2002

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [conll2002](https://huggingface.co/datasets/conll2002) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) as the underlying encoder.

## Model Details

### Model Description

- **Model Type:** SpanMarker
- **Encoder:** [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [conll2002](https://huggingface.co/datasets/conll2002)
- **Languages:** es
- **License:** cc-by-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                                                          |
|:------|:------------------------------------------------------------------|
| LOC   | "Australia", "Victoria", "Melbourne"                              |
| MISC  | "Ley", "Ciudad", "CrimeNet"                                       |
| ORG   | "Commonwealth", "EFE", "Tribunal Supremo"                         |
| PER   | "Abogado General del Estado", "Daryl Williams", "Abogado General" |

## Uses

### Direct Use for Inference

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("alvarobartt/span-marker-roberta-base-bne-conll-2002-es")
# Run inference
entities = model.predict("George Washington estuvo en Washington.")
```

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## Training Details

### Training Set Metrics

| Training set          | Min | Median  | Max  |
|:----------------------|:----|:--------|:-----|
| Sentence length       | 1   | 31.8052 | 1238 |
| Entities per sentence | 0   | 2.2586  | 160  |

### Training Hyperparameters

- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- 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: 2

### Training Results

| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.1188 | 100  | 0.0704          | 0.0                  | 0.0               | 0.0           | 0.8608              |
| 0.2375 | 200  | 0.0279          | 0.8765               | 0.4034            | 0.5525        | 0.9025              |
| 0.3563 | 300  | 0.0158          | 0.8381               | 0.7211            | 0.7752        | 0.9524              |
| 0.4751 | 400  | 0.0134          | 0.8525               | 0.7463            | 0.7959        | 0.9576              |
| 0.5938 | 500  | 0.0130          | 0.8844               | 0.7549            | 0.8145        | 0.9560              |
| 0.7126 | 600  | 0.0119          | 0.8480               | 0.8006            | 0.8236        | 0.9650              |
| 0.8314 | 700  | 0.0098          | 0.8794               | 0.8408            | 0.8597        | 0.9695              |
| 0.9501 | 800  | 0.0091          | 0.8842               | 0.8360            | 0.8594        | 0.9722              |
| 1.0689 | 900  | 0.0093          | 0.8976               | 0.8387            | 0.8672        | 0.9698              |
| 1.1876 | 1000 | 0.0094          | 0.8880               | 0.8517            | 0.8694        | 0.9739              |
| 1.3064 | 1100 | 0.0086          | 0.8920               | 0.8530            | 0.8721        | 0.9737              |
| 1.4252 | 1200 | 0.0092          | 0.8896               | 0.8452            | 0.8668        | 0.9728              |
| 1.5439 | 1300 | 0.0094          | 0.8765               | 0.8313            | 0.8533        | 0.9720              |
| 1.6627 | 1400 | 0.0089          | 0.8805               | 0.8445            | 0.8621        | 0.9720              |
| 1.7815 | 1500 | 0.0088          | 0.8834               | 0.8581            | 0.8706        | 0.9747              |
| 1.9002 | 1600 | 0.0088          | 0.8883               | 0.8547            | 0.8712        | 0.9747              |

### Framework Versions

- Python: 3.10.12
- SpanMarker: 1.3.1.dev
- Transformers: 4.33.2
- PyTorch: 2.0.1+cu118
- Datasets: 2.14.5
- 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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