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Amir13
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xlm-roberta-base-ontonotesv5

Token Classification
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PyTorch
xlm-roberta
Generated from Trainer
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xlm-roberta-base-ontonotesv5 / README.md
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metadata
license: mit
tags:
  - generated_from_trainer
datasets: Amir13/ontonotes5-persian
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: xlm-roberta-base-ontonotesv5
    results: []

xlm-roberta-base-ontonotesv5

This model is a fine-tuned version of xlm-roberta-base on the ontonotes5-persian dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1693
  • Precision: 0.8336
  • Recall: 0.8360
  • F1: 0.8348
  • Accuracy: 0.9753

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
0.1145 1.0 2310 0.1174 0.7717 0.7950 0.7832 0.9697
0.0793 2.0 4620 0.1084 0.8129 0.8108 0.8118 0.9729
0.0627 3.0 6930 0.1078 0.8227 0.8102 0.8164 0.9735
0.047 4.0 9240 0.1132 0.8105 0.8223 0.8164 0.9731
0.0347 5.0 11550 0.1190 0.8185 0.8315 0.8250 0.9742
0.0274 6.0 13860 0.1282 0.8088 0.8387 0.8235 0.9734
0.0202 7.0 16170 0.1329 0.8219 0.8354 0.8286 0.9745
0.0167 8.0 18480 0.1423 0.8147 0.8376 0.8260 0.9742
0.0134 9.0 20790 0.1520 0.8259 0.8308 0.8284 0.9745
0.0097 10.0 23100 0.1627 0.8226 0.8377 0.8300 0.9745
0.0084 11.0 25410 0.1693 0.8336 0.8360 0.8348 0.9753
0.0066 12.0 27720 0.1744 0.8317 0.8359 0.8338 0.9751
0.0053 13.0 30030 0.1764 0.8247 0.8409 0.8327 0.9750
0.004 14.0 32340 0.1797 0.8280 0.8378 0.8328 0.9751
0.004 15.0 34650 0.1809 0.8310 0.8382 0.8346 0.9754

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu116
  • Datasets 2.9.0
  • 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}
}