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--- |
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license: mit |
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base_model: LIAMF-USP/roberta-large-finetuned-race |
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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: bigbird-roberta-large |
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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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# bigbird-roberta-large |
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This model is a fine-tuned version of [LIAMF-USP/roberta-large-finetuned-race](https://huggingface.co./LIAMF-USP/roberta-large-finetuned-race) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6094 |
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- Accuracy: 0.1976 |
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- F1: 0.1757 |
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- Precision: 0.1893 |
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- Recall: 0.1911 |
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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: 8 |
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- eval_batch_size: 16 |
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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: 3 |
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- mixed_precision_training: Native AMP |
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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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| 1.6272 | 0.3233 | 1200 | 1.6094 | 0.2082 | 0.1431 | 0.2007 | 0.1996 | |
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| 1.6218 | 0.6466 | 2400 | 1.6094 | 0.2117 | 0.1340 | 0.1876 | 0.1998 | |
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| 1.6235 | 0.9698 | 3600 | 1.6094 | 0.2104 | 0.1752 | 0.2005 | 0.2015 | |
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| 1.617 | 1.2931 | 4800 | 1.6094 | 0.2088 | 0.1956 | 0.2037 | 0.2028 | |
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| 1.61 | 1.6164 | 6000 | 1.6094 | 0.2091 | 0.1606 | 0.2127 | 0.2024 | |
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| 1.6126 | 1.9397 | 7200 | 1.6094 | 0.2108 | 0.1796 | 0.1965 | 0.2011 | |
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| 1.6174 | 2.2629 | 8400 | 1.6094 | 0.2095 | 0.1833 | 0.2036 | 0.2024 | |
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| 1.6125 | 2.5862 | 9600 | 1.6094 | 0.2097 | 0.1847 | 0.1963 | 0.2016 | |
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| 1.6192 | 2.9095 | 10800 | 1.6094 | 0.1976 | 0.1757 | 0.1893 | 0.1911 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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