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dtorber
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bertweet-base

Text Classification
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roberta
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@@ -17,14 +17,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5645
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- - F1 Macro: 0.8513
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- - F1: 0.9041
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- - F1 Neg: 0.7984
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- - Acc: 0.87
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- - Prec: 0.8974
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- - Recall: 0.9108
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- - Mcc: 0.7027
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  ## Model description
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@@ -50,18 +50,14 @@ The following hyperparameters were used during training:
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  - distributed_type: multi-GPU
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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: 5
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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 | F1 Macro | F1 | F1 Neg | Acc | Prec | Recall | Mcc |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:------:|:------:|
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- | 0.6718 | 1.0 | 614 | 0.6382 | 0.6919 | 0.8136 | 0.5702 | 0.74 | 0.7517 | 0.8867 | 0.4083 |
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- | 0.5134 | 2.0 | 1228 | 0.5523 | 0.7460 | 0.7657 | 0.7263 | 0.7475 | 0.9429 | 0.6445 | 0.5564 |
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- | 0.4637 | 3.0 | 1842 | 0.5460 | 0.8362 | 0.8837 | 0.7887 | 0.85 | 0.8769 | 0.8906 | 0.6726 |
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- | 0.4004 | 4.0 | 2456 | 0.6145 | 0.8160 | 0.8505 | 0.7815 | 0.8225 | 0.9224 | 0.7891 | 0.6471 |
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- | 0.3192 | 5.0 | 3070 | 0.5929 | 0.8476 | 0.8911 | 0.8042 | 0.86 | 0.8876 | 0.8945 | 0.6953 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5414
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+ - F1 Macro: 0.7179
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+ - F1: 0.8053
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+ - F1 Neg: 0.6304
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+ - Acc: 0.745
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+ - Prec: 0.7873
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+ - Recall: 0.8242
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+ - Mcc: 0.4373
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  ## Model description
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  - distributed_type: multi-GPU
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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: 1
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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 | F1 Macro | F1 | F1 Neg | Acc | Prec | Recall | Mcc |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:-----:|:------:|:------:|:------:|
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+ | 0.6385 | 1.0 | 614 | 0.5414 | 0.7179 | 0.8053 | 0.6304 | 0.745 | 0.7873 | 0.8242 | 0.4373 |
 
 
 
 
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  ### Framework versions