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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:
- xtreme
metrics:
- precision
- recall
- f1
widget:
- text: Me llamo Álvaro y vivo en Barcelona (España).
- text: Marie Curie fue profesora en la Universidad de Paris.
- text: La Universidad de Salamanca es la universidad en activo más antigua de España.
pipeline_tag: token-classification
base_model: bert-base-multilingual-cased
model-index:
- name: SpanMarker with bert-base-multilingual-cased on xtreme/PAN-X.es
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: xtreme/PAN-X.es
type: xtreme
split: eval
metrics:
- type: f1
value: 0.9186626746506986
name: F1
- type: precision
value: 0.9231154938993816
name: Precision
- type: recall
value: 0.9142526071842411
name: Recall
---
# SpanMarker with bert-base-multilingual-cased on xtreme/PAN-X.es
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [xtreme/PAN-X.es](https://huggingface.co/datasets/xtreme) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) as the underlying encoder.
## Model Details
### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
- **Maximum Sequence Length:** 512 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [xtreme/PAN-X.es](https://huggingface.co/datasets/xtreme)
- **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 | "Salamanca", "Paris", "Barcelona (España)" |
| ORG | "ONU", "Fútbol Club Barcelona", "Museo Nacional del Prado" |
| PER | "Fray Luis de León", "Leo Messi", "Álvaro Bartolomé" |
## Uses
### Direct Use for Inference
```python
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("alvarobartt/bert-base-multilingual-cased-ner-spanish")
# Run inference
entities = model.predict("Marie Curie fue profesora en la Universidad de Paris.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:----------------------|:----|:-------|:----|
| Sentence length | 3 | 6.4642 | 64 |
| Entities per sentence | 1 | 1.2375 | 24 |
### Training Hyperparameters
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 4
- 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 |
|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.3998 | 1000 | 0.0388 | 0.8761 | 0.8641 | 0.8701 | 0.9223 |
| 0.7997 | 2000 | 0.0326 | 0.8995 | 0.8740 | 0.8866 | 0.9341 |
| 1.1995 | 3000 | 0.0277 | 0.9076 | 0.9019 | 0.9047 | 0.9424 |
| 1.5994 | 4000 | 0.0261 | 0.9143 | 0.9113 | 0.9128 | 0.9473 |
| 1.9992 | 5000 | 0.0234 | 0.9231 | 0.9143 | 0.9187 | 0.9502 |
### Framework Versions
- Python: 3.10.12
- SpanMarker: 1.3.1.dev
- Transformers: 4.33.3
- 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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