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KraMias
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BERTweet

Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
Inference Endpoints
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Training complete

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  1. README.md +12 -12
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -16,8 +16,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2836
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- - Accuracy: 0.9070
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  ## Model description
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@@ -49,16 +49,16 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|
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- | 0.6594 | 0.0994 | 47 | 0.5265 | 0.7661 |
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- | 0.4744 | 0.1987 | 94 | 0.5127 | 0.7736 |
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- | 0.389 | 0.2981 | 141 | 0.4142 | 0.8441 |
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- | 0.4078 | 0.3975 | 188 | 0.3744 | 0.8778 |
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- | 0.344 | 0.4968 | 235 | 0.4091 | 0.8516 |
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- | 0.3149 | 0.5962 | 282 | 0.3440 | 0.8853 |
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- | 0.3883 | 0.6956 | 329 | 0.3599 | 0.8741 |
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- | 0.3254 | 0.7949 | 376 | 0.3196 | 0.9055 |
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- | 0.3261 | 0.8943 | 423 | 0.2935 | 0.9055 |
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- | 0.3308 | 0.9937 | 470 | 0.2836 | 0.9070 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2787
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+ - Accuracy: 0.9025
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|
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+ | 0.6 | 0.0994 | 47 | 0.5233 | 0.7661 |
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+ | 0.4809 | 0.1987 | 94 | 0.5148 | 0.7661 |
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+ | 0.4065 | 0.2981 | 141 | 0.4387 | 0.8373 |
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+ | 0.4104 | 0.3975 | 188 | 0.4005 | 0.8553 |
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+ | 0.3518 | 0.4968 | 235 | 0.4069 | 0.8508 |
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+ | 0.3192 | 0.5962 | 282 | 0.3263 | 0.8883 |
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+ | 0.372 | 0.6956 | 329 | 0.3642 | 0.8816 |
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+ | 0.3068 | 0.7949 | 376 | 0.3216 | 0.9003 |
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+ | 0.3238 | 0.8943 | 423 | 0.2972 | 0.9010 |
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+ | 0.3152 | 0.9937 | 470 | 0.2787 | 0.9025 |
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  ### Framework versions
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