metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: xlm-roberta-base-conll2003-en
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: validation
          args: conll2003
        metrics:
          - name: Precision
            type: precision
            value: 0.9478680879413725
          - name: Recall
            type: recall
            value: 0.9588879528222409
          - name: F1
            type: f1
            value: 0.9533461763966831
          - name: Accuracy
            type: accuracy
            value: 0.9917972098823162

xlm-roberta-base-conll2003-en

This model is a fine-tuned version of xlm-roberta-base on the conll2003 dataset. It achieves the following results on the evaluation set:

Loss: 0.0534
Precision: 0.9479
Recall: 0.9589
F1: 0.9533
Accuracy: 0.9918

Model description

More information needed

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	439	0.0535	0.9131	0.9238	0.9184	0.9865
0.1663	2.0	878	0.0461	0.9305	0.9390	0.9348	0.9887
0.0404	3.0	1317	0.0366	0.9431	0.9501	0.9466	0.9910
0.0252	4.0	1756	0.0381	0.9395	0.9516	0.9455	0.9908
0.0172	5.0	2195	0.0398	0.9409	0.9523	0.9466	0.9911
0.0119	6.0	2634	0.0429	0.9389	0.9560	0.9474	0.9910
0.0091	7.0	3073	0.0463	0.9451	0.9548	0.9500	0.9913
0.0063	8.0	3512	0.0446	0.9478	0.9575	0.9526	0.9919
0.0063	9.0	3951	0.0513	0.9424	0.9569	0.9496	0.9911
0.0049	10.0	4390	0.0494	0.9470	0.9545	0.9507	0.9915
0.0036	11.0	4829	0.0506	0.9477	0.9553	0.9515	0.9917
0.0029	12.0	5268	0.0518	0.9472	0.9586	0.9529	0.9919
0.0026	13.0	5707	0.0530	0.9451	0.9567	0.9508	0.9916
0.0021	14.0	6146	0.0526	0.9468	0.9567	0.9517	0.9917
0.0016	15.0	6585	0.0534	0.9479	0.9589	0.9533	0.9918

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2

Citation

@misc{https://doi.org/10.48550/arxiv.2302.09611,
  doi = {10.48550/ARXIV.2302.09611},
  url = {https://arxiv.org/abs/2302.09611},
  author = {Sartipi, Amir and Fatemi, Afsaneh},
  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English},
  publisher = {arXiv},
  year = {2023},
  copyright = {arXiv.org perpetual, non-exclusive license}
}