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---
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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