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Immoderation_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: Immoderation_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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- # Immoderation_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: 0.7206
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- - Accuracy: 0.5942
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- - Precision: 0.5984
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- - Recall: 0.5545
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- - F1: 0.5756
 
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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 | 0.6935 | 0.5243 | 0.7895 | 0.0564 | 0.1053 |
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- | No log | 2.0 | 268 | 0.6717 | 0.5979 | 0.6188 | 0.4944 | 0.5496 |
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- | No log | 3.0 | 402 | 0.7206 | 0.5942 | 0.5984 | 0.5545 | 0.5756 |
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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-consciousness_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-consciousness_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: 0.6731
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+ - Accuracy: 0.6791
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+ - Precision: 0.6850
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+ - Recall: 0.6998
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+ - F1: 0.6923
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+ - Auc: 0.6784
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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 | 0.6490 | 0.6101 | 0.6945 | 0.4358 | 0.5356 | 0.6158 |
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+ | No log | 2.0 | 268 | 0.6488 | 0.6502 | 0.6996 | 0.5642 | 0.6246 | 0.6530 |
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+ | No log | 3.0 | 402 | 0.6731 | 0.6791 | 0.6850 | 0.6998 | 0.6923 | 0.6784 |
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  ### Framework versions
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