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ajrayman
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Cautiousness_binary

Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
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Training in progress, epoch 1

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  1. README.md +13 -12
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -10,22 +10,23 @@ metrics:
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  - recall
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  - f1
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  model-index:
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- - name: Cautiousness_binary
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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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- # Cautiousness_binary
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  This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5918
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- - Accuracy: 0.6996
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- - Precision: 0.7176
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- - Recall: 0.6836
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- - F1: 0.7002
 
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  ## Model description
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@@ -54,11 +55,11 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | No log | 1.0 | 134 | 0.6005 | 0.6698 | 0.7333 | 0.56 | 0.6351 |
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- | No log | 2.0 | 268 | 0.5905 | 0.6922 | 0.6780 | 0.7618 | 0.7175 |
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- | No log | 3.0 | 402 | 0.5918 | 0.6996 | 0.7176 | 0.6836 | 0.7002 |
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  ### Framework versions
 
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  - recall
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  - f1
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  model-index:
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+ - name: Self_Discipline_binary
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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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+ # Self_Discipline_binary
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  This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6421
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+ - Accuracy: 0.6856
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+ - Precision: 0.6610
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+ - Recall: 0.7424
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+ - F1: 0.6994
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+ - Auc: 0.6865
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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+ | No log | 1.0 | 134 | 0.6211 | 0.6810 | 0.6775 | 0.6723 | 0.6749 | 0.6808 |
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+ | No log | 2.0 | 268 | 0.6428 | 0.6632 | 0.6205 | 0.8144 | 0.7043 | 0.6655 |
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+ | No log | 3.0 | 402 | 0.6421 | 0.6856 | 0.6610 | 0.7424 | 0.6994 | 0.6865 |
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  ### Framework versions
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