---
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 fue a Washington.
pipeline_tag: token-classification
base_model: xlm-roberta-large
model-index:
- name: SpanMarker with xlm-roberta-large on conll2002
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: conll2002
      type: unknown
      split: eval
    metrics:
    - type: f1
      value: 0.8911398300151355
      name: F1
    - type: precision
      value: 0.8981459751232105
      name: Precision
    - type: recall
      value: 0.8842421441774492
      name: Recall
---

# SpanMarker with xlm-roberta-large on conll2002

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. This SpanMarker model uses [xlm-roberta-large](https://huggingface.co./models/xlm-roberta-large) as the underlying encoder.

## Model Details

### Model Description

- **Model Type:** SpanMarker
- **Encoder:** [xlm-roberta-large](https://huggingface.co./models/xlm-roberta-large)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [conll2002](https://huggingface.co./datasets/conll2002)
- **Language:** 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   | "Melbourne", "Australia", "Victoria"                              |
| MISC  | "CrimeNet", "Ciudad", "Ley"                                       |
| ORG   | "Commonwealth", "Tribunal Supremo", "EFE"                         |
| 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-xlm-roberta-large-conll-2002-es")
# Run inference
entities = model.predict("George Washington fue a Washington.")
```
</details>

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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: 1e-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 |
|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.0587 | 50   | 0.4612          | 0.0280               | 0.0007            | 0.0014        | 0.8576              |
| 0.1174 | 100  | 0.0512          | 0.5                  | 0.0002            | 0.0005        | 0.8609              |
| 0.1761 | 150  | 0.0254          | 0.7622               | 0.5494            | 0.6386        | 0.9278              |
| 0.2347 | 200  | 0.0177          | 0.7840               | 0.7135            | 0.7471        | 0.9483              |
| 0.2934 | 250  | 0.0153          | 0.8072               | 0.7944            | 0.8007        | 0.9662              |
| 0.3521 | 300  | 0.0175          | 0.8439               | 0.7544            | 0.7966        | 0.9611              |
| 0.4108 | 350  | 0.0103          | 0.8828               | 0.8108            | 0.8452        | 0.9687              |
| 0.4695 | 400  | 0.0105          | 0.8674               | 0.8433            | 0.8552        | 0.9724              |
| 0.5282 | 450  | 0.0098          | 0.8651               | 0.8477            | 0.8563        | 0.9745              |
| 0.5869 | 500  | 0.0092          | 0.8634               | 0.8306            | 0.8467        | 0.9736              |
| 0.6455 | 550  | 0.0106          | 0.8556               | 0.8581            | 0.8568        | 0.9758              |
| 0.7042 | 600  | 0.0096          | 0.8712               | 0.8521            | 0.8616        | 0.9733              |
| 0.7629 | 650  | 0.0090          | 0.8791               | 0.8420            | 0.8601        | 0.9740              |
| 0.8216 | 700  | 0.0082          | 0.8883               | 0.8799            | 0.8840        | 0.9769              |
| 0.8803 | 750  | 0.0081          | 0.8877               | 0.8604            | 0.8739        | 0.9763              |
| 0.9390 | 800  | 0.0087          | 0.8785               | 0.8738            | 0.8762        | 0.9763              |
| 0.9977 | 850  | 0.0084          | 0.8777               | 0.8653            | 0.8714        | 0.9767              |
| 1.0563 | 900  | 0.0081          | 0.8894               | 0.8713            | 0.8803        | 0.9767              |
| 1.1150 | 950  | 0.0078          | 0.8944               | 0.8708            | 0.8825        | 0.9768              |
| 1.1737 | 1000 | 0.0079          | 0.8973               | 0.8722            | 0.8846        | 0.9776              |
| 1.2324 | 1050 | 0.0080          | 0.8792               | 0.8780            | 0.8786        | 0.9783              |
| 1.2911 | 1100 | 0.0082          | 0.8821               | 0.8574            | 0.8696        | 0.9767              |
| 1.3498 | 1150 | 0.0075          | 0.8928               | 0.8697            | 0.8811        | 0.9774              |
| 1.4085 | 1200 | 0.0076          | 0.8919               | 0.8803            | 0.8860        | 0.9792              |
| 1.4671 | 1250 | 0.0078          | 0.8846               | 0.8695            | 0.8770        | 0.9781              |
| 1.5258 | 1300 | 0.0074          | 0.8944               | 0.8845            | 0.8894        | 0.9792              |
| 1.5845 | 1350 | 0.0076          | 0.8922               | 0.8856            | 0.8889        | 0.9796              |
| 1.6432 | 1400 | 0.0072          | 0.9004               | 0.8799            | 0.8900        | 0.9790              |
| 1.7019 | 1450 | 0.0076          | 0.8944               | 0.8889            | 0.8916        | 0.9800              |
| 1.7606 | 1500 | 0.0074          | 0.8962               | 0.8861            | 0.8911        | 0.9800              |
| 1.8192 | 1550 | 0.0072          | 0.8988               | 0.8886            | 0.8937        | 0.9809              |
| 1.8779 | 1600 | 0.0074          | 0.8962               | 0.8833            | 0.8897        | 0.9797              |
| 1.9366 | 1650 | 0.0071          | 0.8976               | 0.8849            | 0.8912        | 0.9799              |
| 1.9953 | 1700 | 0.0071          | 0.8981               | 0.8842            | 0.8911        | 0.9799              |

### 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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