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bert-base-parsbert-uncased-wnut2017
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on the wnut2017-persian dataset. It achieves the following results on the evaluation set:
- Loss: 0.4473
- Precision: 0.5374
- Recall: 0.4072
- F1: 0.4633
- Accuracy: 0.9375
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 | 106 | 0.3045 | 0.5994 | 0.2506 | 0.3534 | 0.9310 |
| No log | 2.0 | 212 | 0.3051 | 0.5980 | 0.2940 | 0.3942 | 0.9352 |
| No log | 3.0 | 318 | 0.2949 | 0.5284 | 0.3807 | 0.4426 | 0.9369 |
| No log | 4.0 | 424 | 0.3382 | 0.5190 | 0.3940 | 0.4479 | 0.9368 |
| 0.1264 | 5.0 | 530 | 0.3700 | 0.5056 | 0.3783 | 0.4328 | 0.9352 |
| 0.1264 | 6.0 | 636 | 0.3975 | 0.4938 | 0.3867 | 0.4338 | 0.9350 |
| 0.1264 | 7.0 | 742 | 0.4587 | 0.5450 | 0.3795 | 0.4474 | 0.9369 |
| 0.1264 | 8.0 | 848 | 0.4473 | 0.5374 | 0.4072 | 0.4633 | 0.9375 |
| 0.1264 | 9.0 | 954 | 0.4940 | 0.5313 | 0.3578 | 0.4276 | 0.9362 |
| 0.0126 | 10.0 | 1060 | 0.5195 | 0.5631 | 0.3494 | 0.4312 | 0.9365 |
| 0.0126 | 11.0 | 1166 | 0.4825 | 0.5449 | 0.3952 | 0.4581 | 0.9371 |
| 0.0126 | 12.0 | 1272 | 0.4862 | 0.5288 | 0.3976 | 0.4539 | 0.9369 |
| 0.0126 | 13.0 | 1378 | 0.5017 | 0.5459 | 0.3867 | 0.4528 | 0.9373 |
| 0.0126 | 14.0 | 1484 | 0.4963 | 0.5403 | 0.3880 | 0.4516 | 0.9371 |
| 0.0032 | 15.0 | 1590 | 0.5035 | 0.5481 | 0.3843 | 0.4518 | 0.9371 |
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}
}
