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---
language:
- es
license: cc-by-sa-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: Por otro lado, el primer ministro portugués, Antonio Guterres, presidente
de turno del Consejo Europeo, recibió hoy al ministro del Interior de Colombia,
Hugo de la Calle, enviado especial del presidente de su país, Andrés Pastrana.
- text: Los consejeros de la Presidencia, Gaspar Zarrías, de Justicia, Carmen Hermosín,
y de Asuntos Sociales, Isaías Pérez Saldaña, darán comienzo mañana a los turnos
de comparecencias de los miembros del Gobierno andaluz en el Parlamento autonómico
para informar de las líneas de actuación de sus departamentos.
- text: '(SV2147) PP: PROBLEMAS INTERNOS PSOE INTERFIEREN EN POLITICA DE LA JUNTA
Córdoba (EFE).'
- text: Cuando vino a Soria, en febrero de 1998, para sustituir al entonces destituido
Antonio Gómez, estaba dirigiendo al Badajoz B en tercera división y consiguió
con el Numancia la permanencia en la última jornada frente al Hércules.
- text: El ministro ecuatoriano de Defensa, Hugo Unda, aseguró hoy que las Fuerzas
Armadas respetarán la decisión del Parlamento sobre la amnistía para los involucrados
en la asonada golpista del pasado 21 de enero, cuando fue derrocado el presidente
Jamil Mahuad.
pipeline_tag: token-classification
base_model: bert-base-cased
model-index:
- name: SpanMarker with bert-base-cased on conll2002
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: Unknown
type: conll2002
split: test
metrics:
- type: f1
value: 0.8200812536273941
name: F1
- type: precision
value: 0.8331367924528302
name: Precision
- type: recall
value: 0.8074285714285714
name: Recall
---
# SpanMarker with bert-base-cased 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 [bert-base-cased](https://huggingface.co/bert-base-cased) as the underlying encoder.
## Model Details
### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [bert-base-cased](https://huggingface.co/bert-base-cased)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [conll2002](https://huggingface.co/datasets/conll2002)
- **Language:** es
- **License:** cc-by-sa-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 | "Victoria", "Australia", "Melbourne" |
| MISC | "Ley", "Ciudad", "CrimeNet" |
| ORG | "Tribunal Supremo", "EFE", "Commonwealth" |
| PER | "Abogado General del Estado", "Daryl Williams", "Abogado General" |
## Evaluation
### Metrics
| Label | Precision | Recall | F1 |
|:--------|:----------|:-------|:-------|
| **all** | 0.8331 | 0.8074 | 0.8201 |
| LOC | 0.8471 | 0.7759 | 0.8099 |
| MISC | 0.7092 | 0.4264 | 0.5326 |
| ORG | 0.7854 | 0.8558 | 0.8191 |
| PER | 0.9471 | 0.9329 | 0.9400 |
## 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("(SV2147) PP: PROBLEMAS INTERNOS PSOE INTERFIEREN EN POLITICA DE LA JUNTA Córdoba (EFE).")
```
### 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 | 0 | 31.8014 | 1238 |
| Entities per sentence | 0 | 2.2583 | 160 |
### Training Hyperparameters
- learning_rate: 5e-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 |
|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.1164 | 200 | 0.0260 | 0.6907 | 0.5358 | 0.6035 | 0.9264 |
| 0.2328 | 400 | 0.0199 | 0.7567 | 0.6384 | 0.6925 | 0.9414 |
| 0.3491 | 600 | 0.0176 | 0.7773 | 0.7273 | 0.7515 | 0.9563 |
| 0.4655 | 800 | 0.0157 | 0.8066 | 0.7598 | 0.7825 | 0.9601 |
| 0.5819 | 1000 | 0.0158 | 0.8031 | 0.7413 | 0.7710 | 0.9605 |
| 0.6983 | 1200 | 0.0156 | 0.7975 | 0.7598 | 0.7782 | 0.9609 |
| 0.8147 | 1400 | 0.0139 | 0.8210 | 0.7615 | 0.7901 | 0.9625 |
| 0.9310 | 1600 | 0.0129 | 0.8426 | 0.7848 | 0.8127 | 0.9651 |
### Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.18.0
- Tokenizers: 0.15.2
## 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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