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XLNet-Reddit-Toxic-Comment-Classification

Text Classification
Transformers
PyTorch
xlnet
Generated from Trainer
Inference Endpoints
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End of training

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  1. README.md +11 -11
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -20,12 +20,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3448
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- - Rmse: 0.2976
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- - Accuracy: 0.9115
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- - Precision: 0.9153
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- - Recall: 0.9261
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- - F1: 0.9206
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Rmse | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|:------:|
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- | 0.7011 | 1.0 | 1073 | 0.6911 | 0.6674 | 0.5545 | 0.5545 | 1.0 | 0.7134 |
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- | 0.6829 | 2.0 | 2146 | 0.6936 | 0.7447 | 0.4455 | 0.0 | 0.0 | 0.0 |
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- | 0.7004 | 3.0 | 3219 | 0.6876 | 0.6674 | 0.5545 | 0.5545 | 1.0 | 0.7134 |
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- | 0.5187 | 4.0 | 4292 | 0.3967 | 0.3202 | 0.8975 | 0.9117 | 0.9025 | 0.9071 |
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- | 0.3208 | 5.0 | 5365 | 0.3448 | 0.2976 | 0.9115 | 0.9153 | 0.9261 | 0.9206 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2248
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+ - Rmse: 0.2928
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+ - Accuracy: 0.9143
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+ - Precision: 0.9299
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+ - Recall: 0.9143
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+ - F1: 0.9220
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Rmse | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|:------:|
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+ | 0.3656 | 1.0 | 1073 | 0.2248 | 0.2928 | 0.9143 | 0.9299 | 0.9143 | 0.9220 |
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+ | 0.2432 | 2.0 | 2146 | 0.3105 | 0.2912 | 0.9152 | 0.9158 | 0.9328 | 0.9242 |
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+ | 0.1649 | 3.0 | 3219 | 0.3818 | 0.2696 | 0.9273 | 0.9176 | 0.9546 | 0.9357 |
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+ | 0.1075 | 4.0 | 4292 | 0.4398 | 0.2798 | 0.9217 | 0.9049 | 0.9597 | 0.9315 |
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+ | 0.0788 | 5.0 | 5365 | 0.4655 | 0.2847 | 0.9189 | 0.9110 | 0.9462 | 0.9283 |
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  ### Framework versions
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