Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	439	0.0535	0.9131	0.9238	0.9184	0.9865
0.1663	2.0	878	0.0461	0.9305	0.9390	0.9348	0.9887
0.0404	3.0	1317	0.0366	0.9431	0.9501	0.9466	0.9910
0.0252	4.0	1756	0.0381	0.9395	0.9516	0.9455	0.9908
0.0172	5.0	2195	0.0398	0.9409	0.9523	0.9466	0.9911
0.0119	6.0	2634	0.0429	0.9389	0.9560	0.9474	0.9910
0.0091	7.0	3073	0.0463	0.9451	0.9548	0.9500	0.9913
0.0063	8.0	3512	0.0446	0.9478	0.9575	0.9526	0.9919
0.0063	9.0	3951	0.0513	0.9424	0.9569	0.9496	0.9911
0.0049	10.0	4390	0.0494	0.9470	0.9545	0.9507	0.9915
0.0036	11.0	4829	0.0506	0.9477	0.9553	0.9515	0.9917
0.0029	12.0	5268	0.0518	0.9472	0.9586	0.9529	0.9919
0.0026	13.0	5707	0.0530	0.9451	0.9567	0.9508	0.9916
0.0021	14.0	6146	0.0526	0.9468	0.9567	0.9517	0.9917
0.0016	15.0	6585	0.0534	0.9479	0.9589	0.9533	0.9918

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2

Citation

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-en

xlm-roberta-base-conll2003-en

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Citation

Amir13/
xlm-roberta-base-conll2003-en