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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: roberta-finetuned-WebClassification-v2-smalllinguaMultiv2 |
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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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# roberta-finetuned-WebClassification-v2-smalllinguaMultiv2 |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8644 |
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- Accuracy: 0.8387 |
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- F1: 0.8387 |
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- Precision: 0.8387 |
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- Recall: 0.8387 |
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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: 4 |
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- eval_batch_size: 4 |
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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 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 95 | 2.3654 | 0.4409 | 0.4409 | 0.4409 | 0.4409 | |
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| No log | 2.0 | 190 | 1.8455 | 0.5269 | 0.5269 | 0.5269 | 0.5269 | |
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| No log | 3.0 | 285 | 1.4468 | 0.6344 | 0.6344 | 0.6344 | 0.6344 | |
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| No log | 4.0 | 380 | 1.1099 | 0.7419 | 0.7419 | 0.7419 | 0.7419 | |
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| No log | 5.0 | 475 | 1.0515 | 0.7634 | 0.7634 | 0.7634 | 0.7634 | |
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| 1.6355 | 6.0 | 570 | 0.9938 | 0.7312 | 0.7312 | 0.7312 | 0.7312 | |
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| 1.6355 | 7.0 | 665 | 0.8275 | 0.7957 | 0.7957 | 0.7957 | 0.7957 | |
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| 1.6355 | 8.0 | 760 | 0.8344 | 0.7957 | 0.7957 | 0.7957 | 0.7957 | |
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| 1.6355 | 9.0 | 855 | 0.8516 | 0.8065 | 0.8065 | 0.8065 | 0.8065 | |
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| 1.6355 | 10.0 | 950 | 0.8723 | 0.7957 | 0.7957 | 0.7957 | 0.7957 | |
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| 0.2827 | 11.0 | 1045 | 0.8644 | 0.8387 | 0.8387 | 0.8387 | 0.8387 | |
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| 0.2827 | 12.0 | 1140 | 0.9343 | 0.8065 | 0.8065 | 0.8065 | 0.8065 | |
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| 0.2827 | 13.0 | 1235 | 1.0181 | 0.7957 | 0.7957 | 0.7957 | 0.7957 | |
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| 0.2827 | 14.0 | 1330 | 1.0068 | 0.7957 | 0.7957 | 0.7957 | 0.7957 | |
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| 0.2827 | 15.0 | 1425 | 1.0085 | 0.8065 | 0.8065 | 0.8065 | 0.8065 | |
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| 0.0485 | 16.0 | 1520 | 1.0257 | 0.8280 | 0.8280 | 0.8280 | 0.8280 | |
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| 0.0485 | 17.0 | 1615 | 1.0305 | 0.8172 | 0.8172 | 0.8172 | 0.8172 | |
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| 0.0485 | 18.0 | 1710 | 1.0648 | 0.7957 | 0.7957 | 0.7957 | 0.7957 | |
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| 0.0485 | 19.0 | 1805 | 1.0677 | 0.7957 | 0.7957 | 0.7957 | 0.7957 | |
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| 0.0485 | 20.0 | 1900 | 1.0687 | 0.7957 | 0.7957 | 0.7957 | 0.7957 | |
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### Framework versions |
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- Transformers 4.31.0.dev0 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
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