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}
}