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Xtiphyn
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News-classification

Text Classification
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distilbert
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xtiphyn/News-classification

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  1. README.md +9 -8
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -19,9 +19,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1131
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- - Accuracy: 0.7275
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- - F1: 0.7255
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  ## Model description
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@@ -46,17 +46,18 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use OptimizerNames.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: 4
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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 |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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- | 0.9698 | 1.0 | 5872 | 0.9485 | 0.7162 | 0.7105 |
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- | 0.7279 | 2.0 | 11744 | 0.9095 | 0.7329 | 0.7253 |
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- | 0.4669 | 3.0 | 17616 | 1.0053 | 0.7291 | 0.7257 |
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- | 0.2848 | 4.0 | 23488 | 1.1131 | 0.7275 | 0.7255 |
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.3143
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+ - Accuracy: 0.7234
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+ - F1: 0.7225
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  ## Model description
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  - seed: 42
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  - optimizer: Use OptimizerNames.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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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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+ | 1.0029 | 1.0 | 5872 | 0.9577 | 0.7152 | 0.7091 |
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+ | 0.734 | 2.0 | 11744 | 0.9163 | 0.7309 | 0.7233 |
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+ | 0.4823 | 3.0 | 17616 | 1.0005 | 0.7283 | 0.7251 |
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+ | 0.3021 | 4.0 | 23488 | 1.1558 | 0.7252 | 0.7241 |
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+ | 0.1596 | 5.0 | 29360 | 1.3143 | 0.7234 | 0.7225 |
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  ### Framework versions
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