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xlm-roberta-base-conll2003

Token Classification
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PyTorch
xlm-roberta
Generated from Trainer
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metadata
license: mit
tags:
  - generated_from_trainer
datasets: Amir13/conll2003-persian
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: xlm-roberta-base-conll2003
    results: []

xlm-roberta-base-conll2003

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

  • Loss: 0.1579
  • Precision: 0.8794
  • Recall: 0.8745
  • F1: 0.8769
  • Accuracy: 0.9758

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 1.0 430 0.1374 0.8043 0.7966 0.8004 0.9613
0.2862 2.0 860 0.1093 0.8384 0.8482 0.8433 0.9695
0.1043 3.0 1290 0.1121 0.8448 0.8556 0.8502 0.9708
0.0689 4.0 1720 0.1094 0.8635 0.8650 0.8643 0.9737
0.0473 5.0 2150 0.1225 0.8665 0.8625 0.8645 0.9736
0.0342 6.0 2580 0.1186 0.8722 0.8730 0.8726 0.9745
0.0245 7.0 3010 0.1292 0.8802 0.8717 0.8759 0.9755
0.0245 8.0 3440 0.1309 0.8832 0.8689 0.8760 0.9749
0.0177 9.0 3870 0.1388 0.8712 0.8717 0.8715 0.9743
0.0135 10.0 4300 0.1466 0.8699 0.8728 0.8714 0.9752
0.0103 11.0 4730 0.1486 0.8716 0.8747 0.8731 0.9756
0.0081 12.0 5160 0.1521 0.8789 0.8736 0.8762 0.9759
0.007 13.0 5590 0.1546 0.8804 0.8734 0.8769 0.9756
0.0053 14.0 6020 0.1552 0.8750 0.8732 0.8741 0.9756
0.0053 15.0 6450 0.1579 0.8794 0.8745 0.8769 0.9758

Framework versions

  • Transformers 4.27.0.dev0
  • Pytorch 1.13.1+cu116
  • Datasets 2.8.0
  • Tokenizers 0.13.2

Citation

If you used the datasets and models in this repository, please cite it.

@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}
}