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MoGHenry
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bert-base-uncased-tweet-disaster-classification

Text Classification
Transformers
Safetensors
distilbert
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End of training

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  1. README.md +17 -11
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -18,8 +18,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.9586
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- - Accuracy: 0.7951
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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- - train_batch_size: 16
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- - eval_batch_size: 16
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 5
 
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 381 | 0.4382 | 0.8207 |
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- | 0.3996 | 2.0 | 762 | 0.5340 | 0.8122 |
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- | 0.2537 | 3.0 | 1143 | 0.6641 | 0.8148 |
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- | 0.1347 | 4.0 | 1524 | 0.8495 | 0.7866 |
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- | 0.1347 | 5.0 | 1905 | 0.9586 | 0.7951 |
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5396
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+ - Accuracy: 0.8076
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 96 | 0.6598 | 0.7439 |
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+ | No log | 2.0 | 192 | 0.4624 | 0.8011 |
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+ | No log | 3.0 | 288 | 0.4350 | 0.8148 |
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+ | No log | 4.0 | 384 | 0.4326 | 0.8188 |
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+ | No log | 5.0 | 480 | 0.4331 | 0.8247 |
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+ | 0.4631 | 6.0 | 576 | 0.4566 | 0.8227 |
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+ | 0.4631 | 7.0 | 672 | 0.4711 | 0.8194 |
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+ | 0.4631 | 8.0 | 768 | 0.5045 | 0.8102 |
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+ | 0.4631 | 9.0 | 864 | 0.5400 | 0.8050 |
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+ | 0.4631 | 10.0 | 960 | 0.5396 | 0.8076 |
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  ### Framework versions
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