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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: |
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- wnut_17 |
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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-en |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: wnut_17 |
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type: wnut_17 |
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config: wnut_17 |
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split: validation |
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args: wnut_17 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7219662058371735 |
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- name: Recall |
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type: recall |
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value: 0.562200956937799 |
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- name: F1 |
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type: f1 |
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value: 0.6321452589105581 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9589398080467807 |
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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-en |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on [wnut_17](https://huggingface.co/datasets/wnut_17) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2319 |
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- Precision: 0.7220 |
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- Recall: 0.5622 |
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- F1: 0.6321 |
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- Accuracy: 0.9589 |
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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 | 107 | 0.2789 | 0.4679 | 0.3397 | 0.3936 | 0.9408 | |
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| No log | 2.0 | 214 | 0.2092 | 0.6875 | 0.5 | 0.5789 | 0.9518 | |
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| No log | 3.0 | 321 | 0.1968 | 0.6194 | 0.5431 | 0.5787 | 0.9567 | |
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| No log | 4.0 | 428 | 0.2172 | 0.7212 | 0.5383 | 0.6164 | 0.9586 | |
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| 0.1523 | 5.0 | 535 | 0.2319 | 0.7220 | 0.5622 | 0.6321 | 0.9589 | |
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| 0.1523 | 6.0 | 642 | 0.2023 | 0.6180 | 0.5514 | 0.5828 | 0.9577 | |
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| 0.1523 | 7.0 | 749 | 0.2494 | 0.6895 | 0.5419 | 0.6068 | 0.9589 | |
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| 0.1523 | 8.0 | 856 | 0.2844 | 0.7018 | 0.5263 | 0.6015 | 0.9578 | |
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| 0.1523 | 9.0 | 963 | 0.2568 | 0.6808 | 0.5562 | 0.6122 | 0.9592 | |
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| 0.0294 | 10.0 | 1070 | 0.2453 | 0.6718 | 0.5754 | 0.6198 | 0.9596 | |
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| 0.0294 | 11.0 | 1177 | 0.2538 | 0.6933 | 0.5706 | 0.6260 | 0.9600 | |
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| 0.0294 | 12.0 | 1284 | 0.2638 | 0.6865 | 0.5658 | 0.6203 | 0.9593 | |
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| 0.0294 | 13.0 | 1391 | 0.2744 | 0.6764 | 0.5526 | 0.6083 | 0.9587 | |
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| 0.0294 | 14.0 | 1498 | 0.2714 | 0.6812 | 0.5622 | 0.6160 | 0.9590 | |
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| 0.0135 | 15.0 | 1605 | 0.2724 | 0.6830 | 0.5670 | 0.6196 | 0.9593 | |
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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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``` |
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