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---
base_model: FacebookAI/xlm-roberta-large-finetuned-conll03-english
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xml-roberta-large-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2002
type: conll2002
config: es
split: validation
args: es
metrics:
- name: Precision
type: precision
value: 0.880600409370025
- name: Recall
type: recall
value: 0.8897058823529411
- name: F1
type: f1
value: 0.8851297291118985
- name: Accuracy
type: accuracy
value: 0.9806463992982264
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xml-roberta-large-finetuned-ner
Este es modelo resultado de un finetuning de
[FacebookAI/xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english) sobre el conll2002 dataset.
Los siguientes son los resultados sobre el conjunto de evaluación:
- Loss: 0.1364
- Precision: 0.8806
- Recall: 0.8897
- F1: 0.8851
- Accuracy: 0.9806
## Model description
Este es el modelo más grande de roberta [FacebookAI/xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english)-
Este modelo fue ajustado usando el framework Kaggle [https://www.kaggle.com/settings]. Para realizar el preentrenamiento del modelo se tuvo que crear un directorio temporal en Kaggle
con el fin de almacenar de manera temoporal el modelo que pesa alrededor de 35 Gz.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0743 | 1.0 | 2081 | 0.1131 | 0.8385 | 0.8587 | 0.8485 | 0.9771 |
| 0.049 | 2.0 | 4162 | 0.1429 | 0.8492 | 0.8564 | 0.8528 | 0.9756 |
| 0.031 | 3.0 | 6243 | 0.1298 | 0.8758 | 0.8817 | 0.8787 | 0.9800 |
| 0.0185 | 4.0 | 8324 | 0.1279 | 0.8827 | 0.8890 | 0.8859 | 0.9808 |
| 0.0125 | 5.0 | 10405 | 0.1364 | 0.8806 | 0.8897 | 0.8851 | 0.9806 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
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