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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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- banking77 |
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metrics: |
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- accuracy |
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model-index: |
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- name: xlm-roberta-base-banking77-classification |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: banking77 |
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type: banking77 |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9321428571428572 |
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widget: |
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- text: 'Can I track the card you sent to me? ' |
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example_title: Card Arrival Example - English |
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- text: 'Posso tracciare la carta che mi avete spedito? ' |
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example_title: Card Arrival Example - Italian |
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- text: Can you explain your exchange rate policy to me? |
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example_title: Exchange Rate Example - English |
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- text: Potete spiegarmi la vostra politica dei tassi di cambio? |
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example_title: Exchange Rate Example - Italian |
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- text: I can't pay by my credit card |
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example_title: Card Not Working Example - English |
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- text: Non posso pagare con la mia carta di credito |
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example_title: Card Not Working Example - Italian |
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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-banking77-classification |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the banking77 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3034 |
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- Accuracy: 0.9321 |
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- F1 Score: 0.9321 |
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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: 64 |
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- eval_batch_size: 64 |
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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: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| |
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| 3.8002 | 1.0 | 157 | 2.7771 | 0.5159 | 0.4483 | |
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| 2.4006 | 2.0 | 314 | 1.6937 | 0.7140 | 0.6720 | |
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| 1.4633 | 3.0 | 471 | 1.0385 | 0.8308 | 0.8153 | |
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| 0.9234 | 4.0 | 628 | 0.7008 | 0.8789 | 0.8761 | |
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| 0.6163 | 5.0 | 785 | 0.5029 | 0.9068 | 0.9063 | |
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| 0.4282 | 6.0 | 942 | 0.4084 | 0.9123 | 0.9125 | |
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| 0.3203 | 7.0 | 1099 | 0.3515 | 0.9253 | 0.9253 | |
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| 0.245 | 8.0 | 1256 | 0.3295 | 0.9227 | 0.9225 | |
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| 0.1863 | 9.0 | 1413 | 0.3092 | 0.9269 | 0.9269 | |
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| 0.1518 | 10.0 | 1570 | 0.2901 | 0.9338 | 0.9338 | |
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| 0.1179 | 11.0 | 1727 | 0.2938 | 0.9318 | 0.9319 | |
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| 0.0969 | 12.0 | 1884 | 0.2906 | 0.9328 | 0.9328 | |
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| 0.0805 | 13.0 | 2041 | 0.2963 | 0.9295 | 0.9295 | |
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| 0.063 | 14.0 | 2198 | 0.2998 | 0.9289 | 0.9288 | |
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| 0.0554 | 15.0 | 2355 | 0.2933 | 0.9351 | 0.9349 | |
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| 0.046 | 16.0 | 2512 | 0.2960 | 0.9328 | 0.9326 | |
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| 0.04 | 17.0 | 2669 | 0.3032 | 0.9318 | 0.9318 | |
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| 0.035 | 18.0 | 2826 | 0.3061 | 0.9312 | 0.9312 | |
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| 0.0317 | 19.0 | 2983 | 0.3030 | 0.9331 | 0.9330 | |
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| 0.0315 | 20.0 | 3140 | 0.3034 | 0.9321 | 0.9321 | |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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