README.md

metadata

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

xml-roberta-large-finetuned-ner

Este es modelo resultado de un finetuning de 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- 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