Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	107	0.2789	0.4679	0.3397	0.3936	0.9408
No log	2.0	214	0.2092	0.6875	0.5	0.5789	0.9518
No log	3.0	321	0.1968	0.6194	0.5431	0.5787	0.9567
No log	4.0	428	0.2172	0.7212	0.5383	0.6164	0.9586
0.1523	5.0	535	0.2319	0.7220	0.5622	0.6321	0.9589
0.1523	6.0	642	0.2023	0.6180	0.5514	0.5828	0.9577
0.1523	7.0	749	0.2494	0.6895	0.5419	0.6068	0.9589
0.1523	8.0	856	0.2844	0.7018	0.5263	0.6015	0.9578
0.1523	9.0	963	0.2568	0.6808	0.5562	0.6122	0.9592
0.0294	10.0	1070	0.2453	0.6718	0.5754	0.6198	0.9596
0.0294	11.0	1177	0.2538	0.6933	0.5706	0.6260	0.9600
0.0294	12.0	1284	0.2638	0.6865	0.5658	0.6203	0.9593
0.0294	13.0	1391	0.2744	0.6764	0.5526	0.6083	0.9587
0.0294	14.0	1498	0.2714	0.6812	0.5622	0.6160	0.9590
0.0135	15.0	1605	0.2724	0.6830	0.5670	0.6196	0.9593

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.

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-wnut2017-en

xlm-roberta-base-wnut2017-en

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Citation

Amir13/
xlm-roberta-base-wnut2017-en