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End of training

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README.md ADDED
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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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+
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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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+
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+ # bigbird-roberta-large
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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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