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--- |
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license: mit |
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base_model: facebook/xlm-roberta-xl |
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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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model-index: |
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- name: 5e-6_xlm-R-xl_Conspiracy_training_with_callbacks |
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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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# 5e-6_xlm-R-xl_Conspiracy_training_with_callbacks |
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This model is a fine-tuned version of [facebook/xlm-roberta-xl](https://huggingface.co/facebook/xlm-roberta-xl) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0624 |
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- Macro F1: 0.9918 |
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- Micro F1: 0.9919 |
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- Accuracy: 0.9919 |
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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: 5e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:| |
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| 0.6122 | 1.0 | 502 | 0.2712 | 0.9549 | 0.9559 | 0.9559 | |
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| 0.1503 | 2.0 | 1004 | 0.1047 | 0.9741 | 0.9744 | 0.9744 | |
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| 0.0508 | 3.0 | 1506 | 0.0734 | 0.9835 | 0.9837 | 0.9837 | |
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| 0.0226 | 4.0 | 2008 | 0.0837 | 0.9811 | 0.9814 | 0.9814 | |
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| 0.014 | 5.0 | 2510 | 0.0562 | 0.9882 | 0.9884 | 0.9884 | |
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| 0.0014 | 6.0 | 3012 | 0.0514 | 0.9894 | 0.9895 | 0.9895 | |
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| 0.0016 | 7.0 | 3514 | 0.0501 | 0.9918 | 0.9919 | 0.9919 | |
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| 0.0002 | 8.0 | 4016 | 0.0554 | 0.9918 | 0.9919 | 0.9919 | |
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| 0.0001 | 9.0 | 4518 | 0.0607 | 0.9906 | 0.9907 | 0.9907 | |
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| 0.0001 | 10.0 | 5020 | 0.0856 | 0.9859 | 0.9861 | 0.9861 | |
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| 0.0143 | 11.0 | 5522 | 0.0377 | 0.9929 | 0.9930 | 0.9930 | |
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| 0.0001 | 12.0 | 6024 | 0.0538 | 0.9918 | 0.9919 | 0.9919 | |
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| 0.0001 | 13.0 | 6526 | 0.0568 | 0.9918 | 0.9919 | 0.9919 | |
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| 0.0012 | 14.0 | 7028 | 0.0582 | 0.9918 | 0.9919 | 0.9919 | |
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| 0.0 | 15.0 | 7530 | 0.0500 | 0.9929 | 0.9930 | 0.9930 | |
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| 0.0001 | 16.0 | 8032 | 0.0649 | 0.9918 | 0.9919 | 0.9919 | |
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| 0.0 | 17.0 | 8534 | 0.0649 | 0.9918 | 0.9919 | 0.9919 | |
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| 0.0 | 18.0 | 9036 | 0.0648 | 0.9918 | 0.9919 | 0.9919 | |
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| 0.0009 | 19.0 | 9538 | 0.0621 | 0.9918 | 0.9919 | 0.9919 | |
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| 0.0 | 20.0 | 10040 | 0.0624 | 0.9918 | 0.9919 | 0.9919 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
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