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.

bibtex

@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}
}

Amir13/xlm-roberta-base-conll2003

xlm-roberta-base-conll2003

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Citation

Amir13/
xlm-roberta-base-conll2003