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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: Amir13/wnut2017-persian |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: xlm-roberta-base-wnut2017 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# xlm-roberta-base-wnut2017 |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [wnut2017-persian](https://huggingface.co/datasets/Amir13/wnut2017-persian) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2943 |
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- Precision: 0.5430 |
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- Recall: 0.4181 |
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- F1: 0.4724 |
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- Accuracy: 0.9379 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 106 | 0.3715 | 0.0667 | 0.0012 | 0.0024 | 0.9119 | |
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| No log | 2.0 | 212 | 0.3279 | 0.3482 | 0.1783 | 0.2359 | 0.9217 | |
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| No log | 3.0 | 318 | 0.3008 | 0.5574 | 0.3627 | 0.4394 | 0.9344 | |
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| No log | 4.0 | 424 | 0.2884 | 0.5226 | 0.3614 | 0.4274 | 0.9363 | |
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| 0.2149 | 5.0 | 530 | 0.2943 | 0.5430 | 0.4181 | 0.4724 | 0.9379 | |
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| 0.2149 | 6.0 | 636 | 0.3180 | 0.5338 | 0.3711 | 0.4378 | 0.9377 | |
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| 0.2149 | 7.0 | 742 | 0.3090 | 0.4993 | 0.4277 | 0.4607 | 0.9365 | |
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| 0.2149 | 8.0 | 848 | 0.3300 | 0.5300 | 0.4048 | 0.4590 | 0.9380 | |
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| 0.2149 | 9.0 | 954 | 0.3365 | 0.4938 | 0.3843 | 0.4322 | 0.9367 | |
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| 0.0623 | 10.0 | 1060 | 0.3363 | 0.5028 | 0.4313 | 0.4643 | 0.9363 | |
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| 0.0623 | 11.0 | 1166 | 0.3567 | 0.4992 | 0.3880 | 0.4366 | 0.9356 | |
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| 0.0623 | 12.0 | 1272 | 0.3681 | 0.5164 | 0.3988 | 0.4500 | 0.9375 | |
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| 0.0623 | 13.0 | 1378 | 0.3698 | 0.5086 | 0.3928 | 0.4432 | 0.9376 | |
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| 0.0623 | 14.0 | 1484 | 0.3690 | 0.5157 | 0.4157 | 0.4603 | 0.9380 | |
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| 0.0303 | 15.0 | 1590 | 0.3744 | 0.5045 | 0.4072 | 0.4507 | 0.9375 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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### Citation |
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If you used the datasets and models in this repository, please cite it. |
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```bibtex |
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@misc{https://doi.org/10.48550/arxiv.2302.09611, |
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doi = {10.48550/ARXIV.2302.09611}, |
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url = {https://arxiv.org/abs/2302.09611}, |
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author = {Sartipi, Amir and Fatemi, Afsaneh}, |
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keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English}, |
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publisher = {arXiv}, |
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year = {2023}, |
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copyright = {arXiv.org perpetual, non-exclusive license} |
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} |
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``` |
