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tmnam20
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videberta-base_1024

Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
Inference Endpoints
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End of training

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  1. README.md +21 -21
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -16,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Fsoft-AIC/videberta-base](https://huggingface.co/Fsoft-AIC/videberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5634
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  - Accuracy: 0.75
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  ## Model description
@@ -52,26 +52,26 @@ 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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- | 0.6979 | 0.1 | 50 | 0.6724 | 0.75 |
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- | 0.6979 | 0.21 | 100 | 0.6272 | 0.75 |
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- | 0.6979 | 0.31 | 150 | 0.5727 | 0.75 |
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- | 0.6979 | 0.41 | 200 | 0.5659 | 0.75 |
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- | 0.6979 | 0.52 | 250 | 0.5675 | 0.75 |
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- | 0.6979 | 0.62 | 300 | 0.5633 | 0.75 |
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- | 0.6979 | 0.72 | 350 | 0.5931 | 0.75 |
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- | 0.6979 | 0.83 | 400 | 0.5644 | 0.75 |
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- | 0.6979 | 0.93 | 450 | 0.5633 | 0.75 |
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- | 0.6979 | 1.03 | 500 | 0.5926 | 0.75 |
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- | 0.6979 | 1.14 | 550 | 0.5649 | 0.75 |
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- | 0.6979 | 1.24 | 600 | 0.5628 | 0.75 |
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- | 0.6979 | 1.34 | 650 | 0.5631 | 0.75 |
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- | 0.6979 | 1.45 | 700 | 0.5688 | 0.75 |
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- | 0.6979 | 1.55 | 750 | 0.5624 | 0.75 |
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- | 0.6979 | 1.65 | 800 | 0.5630 | 0.75 |
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- | 0.6979 | 1.76 | 850 | 0.5628 | 0.75 |
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- | 0.6979 | 1.86 | 900 | 0.5624 | 0.75 |
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- | 0.6979 | 1.96 | 950 | 0.5637 | 0.75 |
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- | 0.5706 | 2.07 | 1000 | 0.5634 | 0.75 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [Fsoft-AIC/videberta-base](https://huggingface.co/Fsoft-AIC/videberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5931
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  - Accuracy: 0.75
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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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+ | 0.5982 | 0.1 | 50 | 0.6297 | 0.75 |
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+ | 0.5505 | 0.21 | 100 | 0.5696 | 0.75 |
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+ | 0.5838 | 0.31 | 150 | 0.5629 | 0.75 |
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+ | 0.5925 | 0.41 | 200 | 0.5931 | 0.75 |
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+ | 0.7003 | 0.52 | 250 | 0.5931 | 0.75 |
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+ | 0.606 | 0.62 | 300 | 0.5931 | 0.75 |
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+ | 0.6744 | 0.72 | 350 | 0.5931 | 0.75 |
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+ | 0.6448 | 0.83 | 400 | 0.5931 | 0.75 |
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+ | 0.7365 | 0.93 | 450 | 0.5931 | 0.75 |
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+ | 0.6083 | 1.03 | 500 | 0.5931 | 0.75 |
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+ | 0.6217 | 1.14 | 550 | 0.5931 | 0.75 |
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+ | 0.642 | 1.24 | 600 | 0.5931 | 0.75 |
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+ | 0.6433 | 1.34 | 650 | 0.5931 | 0.75 |
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+ | 0.7497 | 1.45 | 700 | 0.5931 | 0.75 |
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+ | 0.6385 | 1.55 | 750 | 0.5931 | 0.75 |
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+ | 0.6581 | 1.65 | 800 | 0.5931 | 0.75 |
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+ | 0.6201 | 1.76 | 850 | 0.5931 | 0.75 |
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+ | 0.6424 | 1.86 | 900 | 0.5931 | 0.75 |
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+ | 0.619 | 1.96 | 950 | 0.5931 | 0.75 |
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+ | 0.6807 | 2.07 | 1000 | 0.5931 | 0.75 |
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  ### Framework versions
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