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Assertiveness_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: Assertiveness_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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- # Assertiveness_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: 0.7299
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- - Accuracy: 0.6048
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- - Precision: 0.6210
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- - Recall: 0.5662
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- - F1: 0.5923
 
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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 | 0.6743 | 0.5797 | 0.5928 | 0.5460 | 0.5684 |
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- | No log | 2.0 | 268 | 0.6699 | 0.5722 | 0.6177 | 0.4099 | 0.4928 |
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- | No log | 3.0 | 402 | 0.7299 | 0.6048 | 0.6210 | 0.5662 | 0.5923 |
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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: Gregariousness_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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+ # Gregariousness_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: 0.6050
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+ - Accuracy: 0.6794
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+ - Precision: 0.6869
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+ - Recall: 0.5980
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+ - F1: 0.6394
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+ - Auc: 0.6756
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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 | 0.6922 | 0.4753 | 0.4753 | 1.0 | 0.6443 | 0.5 |
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+ | No log | 2.0 | 268 | 0.6288 | 0.6207 | 0.7696 | 0.2882 | 0.4194 | 0.6050 |
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+ | No log | 3.0 | 402 | 0.6050 | 0.6794 | 0.6869 | 0.5980 | 0.6394 | 0.6756 |
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  ### Framework versions
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