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xlm-roberta-base-ontonotesv5
This model is a fine-tuned version of xlm-roberta-base on the ontonotes5-persian dataset. It achieves the following results on the evaluation set:
- Loss: 0.1693
- Precision: 0.8336
- Recall: 0.8360
- F1: 0.8348
- Accuracy: 0.9753
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 |
|---|---|---|---|---|---|---|---|
| 0.1145 | 1.0 | 2310 | 0.1174 | 0.7717 | 0.7950 | 0.7832 | 0.9697 |
| 0.0793 | 2.0 | 4620 | 0.1084 | 0.8129 | 0.8108 | 0.8118 | 0.9729 |
| 0.0627 | 3.0 | 6930 | 0.1078 | 0.8227 | 0.8102 | 0.8164 | 0.9735 |
| 0.047 | 4.0 | 9240 | 0.1132 | 0.8105 | 0.8223 | 0.8164 | 0.9731 |
| 0.0347 | 5.0 | 11550 | 0.1190 | 0.8185 | 0.8315 | 0.8250 | 0.9742 |
| 0.0274 | 6.0 | 13860 | 0.1282 | 0.8088 | 0.8387 | 0.8235 | 0.9734 |
| 0.0202 | 7.0 | 16170 | 0.1329 | 0.8219 | 0.8354 | 0.8286 | 0.9745 |
| 0.0167 | 8.0 | 18480 | 0.1423 | 0.8147 | 0.8376 | 0.8260 | 0.9742 |
| 0.0134 | 9.0 | 20790 | 0.1520 | 0.8259 | 0.8308 | 0.8284 | 0.9745 |
| 0.0097 | 10.0 | 23100 | 0.1627 | 0.8226 | 0.8377 | 0.8300 | 0.9745 |
| 0.0084 | 11.0 | 25410 | 0.1693 | 0.8336 | 0.8360 | 0.8348 | 0.9753 |
| 0.0066 | 12.0 | 27720 | 0.1744 | 0.8317 | 0.8359 | 0.8338 | 0.9751 |
| 0.0053 | 13.0 | 30030 | 0.1764 | 0.8247 | 0.8409 | 0.8327 | 0.9750 |
| 0.004 | 14.0 | 32340 | 0.1797 | 0.8280 | 0.8378 | 0.8328 | 0.9751 |
| 0.004 | 15.0 | 34650 | 0.1809 | 0.8310 | 0.8382 | 0.8346 | 0.9754 |
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}
}
