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xlm-roberta-base-wnut2017

Token Classification
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xlm-roberta
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xlm-roberta-base-wnut2017 / README.md
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metadata
license: mit
tags:
  - generated_from_trainer
datasets: Amir13/wnut2017-persian
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: xlm-roberta-base-wnut2017
    results: []

xlm-roberta-base-wnut2017

This model is a fine-tuned version of xlm-roberta-base on the wnut2017-persian dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2943
  • Precision: 0.5430
  • Recall: 0.4181
  • F1: 0.4724
  • Accuracy: 0.9379

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 1.0 106 0.3715 0.0667 0.0012 0.0024 0.9119
No log 2.0 212 0.3279 0.3482 0.1783 0.2359 0.9217
No log 3.0 318 0.3008 0.5574 0.3627 0.4394 0.9344
No log 4.0 424 0.2884 0.5226 0.3614 0.4274 0.9363
0.2149 5.0 530 0.2943 0.5430 0.4181 0.4724 0.9379
0.2149 6.0 636 0.3180 0.5338 0.3711 0.4378 0.9377
0.2149 7.0 742 0.3090 0.4993 0.4277 0.4607 0.9365
0.2149 8.0 848 0.3300 0.5300 0.4048 0.4590 0.9380
0.2149 9.0 954 0.3365 0.4938 0.3843 0.4322 0.9367
0.0623 10.0 1060 0.3363 0.5028 0.4313 0.4643 0.9363
0.0623 11.0 1166 0.3567 0.4992 0.3880 0.4366 0.9356
0.0623 12.0 1272 0.3681 0.5164 0.3988 0.4500 0.9375
0.0623 13.0 1378 0.3698 0.5086 0.3928 0.4432 0.9376
0.0623 14.0 1484 0.3690 0.5157 0.4157 0.4603 0.9380
0.0303 15.0 1590 0.3744 0.5045 0.4072 0.4507 0.9375

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}
}