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tf_disilbert_binary

Text Classification
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generated_from_keras_callback
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  1. README.md +54 -54
  2. tf_model.h5 +1 -1
README.md CHANGED
@@ -15,10 +15,10 @@ probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [veriga/tf_disilbert](https://huggingface.co/veriga/tf_disilbert) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Train Loss: 0.6925
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- - Train Sparse Categorical Accuracy: 0.5314
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  - Validation Loss: 0.6883
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- - Validation Sparse Categorical Accuracy: 0.5495
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  - Epoch: 49
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  ## Model description
@@ -38,63 +38,63 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
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  - training_precision: float32
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  ### Training results
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  | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
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  |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
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- | 0.6946 | 0.5250 | 0.6884 | 0.5495 | 0 |
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- | 0.6962 | 0.5286 | 0.6897 | 0.5495 | 1 |
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- | 0.6962 | 0.5176 | 0.6895 | 0.5477 | 2 |
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- | 0.6955 | 0.5276 | 0.6897 | 0.5495 | 3 |
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- | 0.6945 | 0.5265 | 0.6912 | 0.5495 | 4 |
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- | 0.6961 | 0.5263 | 0.6904 | 0.5503 | 5 |
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- | 0.6947 | 0.5242 | 0.6881 | 0.5503 | 6 |
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- | 0.6958 | 0.5201 | 0.6896 | 0.5451 | 7 |
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- | 0.6929 | 0.5258 | 0.6926 | 0.5495 | 8 |
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- | 0.6938 | 0.5277 | 0.6883 | 0.5521 | 9 |
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- | 0.6936 | 0.5277 | 0.6881 | 0.5503 | 10 |
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- | 0.6939 | 0.5271 | 0.6889 | 0.5486 | 11 |
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- | 0.6958 | 0.5284 | 0.6882 | 0.5495 | 12 |
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- | 0.6969 | 0.5186 | 0.6882 | 0.5495 | 13 |
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- | 0.6936 | 0.5332 | 0.6881 | 0.5503 | 14 |
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- | 0.6935 | 0.5344 | 0.6888 | 0.5469 | 15 |
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- | 0.6956 | 0.5232 | 0.6891 | 0.5469 | 16 |
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- | 0.6938 | 0.5268 | 0.6883 | 0.5495 | 17 |
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- | 0.6939 | 0.5260 | 0.6995 | 0.5495 | 18 |
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- | 0.6948 | 0.5248 | 0.6886 | 0.5486 | 19 |
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- | 0.6933 | 0.5317 | 0.6916 | 0.5477 | 20 |
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- | 0.6942 | 0.5223 | 0.6884 | 0.5512 | 21 |
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- | 0.6952 | 0.5236 | 0.6890 | 0.5469 | 22 |
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- | 0.6931 | 0.5275 | 0.6892 | 0.5512 | 23 |
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- | 0.6952 | 0.5297 | 0.6881 | 0.5503 | 24 |
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- | 0.6937 | 0.5291 | 0.6886 | 0.5477 | 25 |
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- | 0.6929 | 0.5272 | 0.6882 | 0.5512 | 26 |
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- | 0.6950 | 0.5230 | 0.6883 | 0.5503 | 27 |
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- | 0.6942 | 0.5316 | 0.6889 | 0.5486 | 28 |
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- | 0.6942 | 0.5270 | 0.6899 | 0.5486 | 29 |
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- | 0.6940 | 0.5312 | 0.6895 | 0.5486 | 30 |
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- | 0.6941 | 0.5316 | 0.6884 | 0.5512 | 31 |
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- | 0.6924 | 0.5330 | 0.6882 | 0.5503 | 32 |
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- | 0.6923 | 0.5328 | 0.6900 | 0.5503 | 33 |
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- | 0.6944 | 0.5308 | 0.6888 | 0.5469 | 34 |
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- | 0.6927 | 0.5349 | 0.6911 | 0.5477 | 35 |
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- | 0.6939 | 0.5286 | 0.6908 | 0.5486 | 36 |
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- | 0.6935 | 0.5282 | 0.6889 | 0.5477 | 37 |
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- | 0.6924 | 0.5289 | 0.6884 | 0.5503 | 38 |
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- | 0.6934 | 0.5304 | 0.6882 | 0.5512 | 39 |
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- | 0.6939 | 0.5227 | 0.6899 | 0.5495 | 40 |
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- | 0.6928 | 0.5280 | 0.6884 | 0.5486 | 41 |
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- | 0.6916 | 0.5379 | 0.6898 | 0.5495 | 42 |
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- | 0.6932 | 0.5236 | 0.6914 | 0.5503 | 43 |
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- | 0.6926 | 0.5333 | 0.6892 | 0.5512 | 44 |
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- | 0.6934 | 0.5282 | 0.6888 | 0.5469 | 45 |
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- | 0.6934 | 0.5301 | 0.6879 | 0.5512 | 46 |
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- | 0.6924 | 0.5354 | 0.6883 | 0.5503 | 47 |
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- | 0.6937 | 0.5311 | 0.6879 | 0.5512 | 48 |
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- | 0.6925 | 0.5314 | 0.6883 | 0.5495 | 49 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [veriga/tf_disilbert](https://huggingface.co/veriga/tf_disilbert) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Train Loss: 0.6901
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+ - Train Sparse Categorical Accuracy: 0.5457
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  - Validation Loss: 0.6883
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+ - Validation Sparse Categorical Accuracy: 0.5503
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  - Epoch: 49
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  ## Model description
 
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - optimizer: {'name': 'Adam', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
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  - training_precision: float32
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  ### Training results
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  | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
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  |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
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+ | 0.6913 | 0.5377 | 0.6900 | 0.5451 | 0 |
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+ | 0.6924 | 0.5308 | 0.6887 | 0.5477 | 1 |
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+ | 0.6921 | 0.5335 | 0.6885 | 0.5512 | 2 |
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+ | 0.6915 | 0.5361 | 0.6899 | 0.5486 | 3 |
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+ | 0.6909 | 0.5317 | 0.6883 | 0.5495 | 4 |
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+ | 0.6911 | 0.5359 | 0.6889 | 0.5469 | 5 |
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+ | 0.6915 | 0.5338 | 0.6883 | 0.5495 | 6 |
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+ | 0.6914 | 0.5397 | 0.6899 | 0.5503 | 7 |
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+ | 0.6907 | 0.5407 | 0.6883 | 0.5495 | 8 |
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+ | 0.6912 | 0.5395 | 0.6885 | 0.5495 | 9 |
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+ | 0.6906 | 0.5351 | 0.6886 | 0.5477 | 10 |
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+ | 0.6913 | 0.5403 | 0.6882 | 0.5503 | 11 |
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+ | 0.6921 | 0.5349 | 0.6886 | 0.5477 | 12 |
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+ | 0.6907 | 0.5404 | 0.6906 | 0.5495 | 13 |
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+ | 0.6906 | 0.5328 | 0.6885 | 0.5486 | 14 |
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+ | 0.6901 | 0.5389 | 0.6881 | 0.5512 | 15 |
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+ | 0.6906 | 0.5401 | 0.6892 | 0.5469 | 16 |
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+ | 0.6908 | 0.5422 | 0.6889 | 0.5495 | 17 |
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+ | 0.6912 | 0.5397 | 0.6888 | 0.5486 | 18 |
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+ | 0.6919 | 0.5331 | 0.6886 | 0.5486 | 19 |
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+ | 0.6915 | 0.5427 | 0.6896 | 0.5469 | 20 |
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+ | 0.6919 | 0.5397 | 0.6922 | 0.5486 | 21 |
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+ | 0.6909 | 0.5367 | 0.6884 | 0.5495 | 22 |
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+ | 0.6913 | 0.5345 | 0.6911 | 0.5503 | 23 |
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+ | 0.6913 | 0.5403 | 0.6887 | 0.5486 | 24 |
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+ | 0.6898 | 0.5439 | 0.6885 | 0.5486 | 25 |
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+ | 0.6908 | 0.5404 | 0.6897 | 0.5503 | 26 |
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+ | 0.6911 | 0.5405 | 0.6884 | 0.5495 | 27 |
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+ | 0.6912 | 0.5395 | 0.6894 | 0.5495 | 28 |
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+ | 0.6908 | 0.5405 | 0.6879 | 0.5512 | 29 |
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+ | 0.6903 | 0.5384 | 0.6908 | 0.5503 | 30 |
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+ | 0.6916 | 0.5318 | 0.6897 | 0.5486 | 31 |
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+ | 0.6911 | 0.5398 | 0.6884 | 0.5512 | 32 |
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+ | 0.6903 | 0.5395 | 0.6886 | 0.5477 | 33 |
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+ | 0.6909 | 0.5375 | 0.6887 | 0.5495 | 34 |
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+ | 0.6909 | 0.5416 | 0.6889 | 0.5469 | 35 |
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+ | 0.6908 | 0.5399 | 0.6883 | 0.5503 | 36 |
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+ | 0.6910 | 0.5382 | 0.6884 | 0.5486 | 37 |
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+ | 0.6910 | 0.5421 | 0.6898 | 0.5486 | 38 |
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+ | 0.6903 | 0.5383 | 0.6888 | 0.5495 | 39 |
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+ | 0.6907 | 0.5426 | 0.6905 | 0.5486 | 40 |
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+ | 0.6900 | 0.5437 | 0.6890 | 0.5495 | 41 |
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+ | 0.6908 | 0.5384 | 0.6888 | 0.5486 | 42 |
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+ | 0.6904 | 0.5407 | 0.6887 | 0.5495 | 43 |
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+ | 0.6910 | 0.5408 | 0.6906 | 0.5495 | 44 |
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+ | 0.6903 | 0.5398 | 0.6880 | 0.5521 | 45 |
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+ | 0.6906 | 0.5421 | 0.6885 | 0.5486 | 46 |
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+ | 0.6906 | 0.5378 | 0.6901 | 0.5503 | 47 |
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+ | 0.6918 | 0.5378 | 0.6893 | 0.5486 | 48 |
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+ | 0.6901 | 0.5457 | 0.6883 | 0.5503 | 49 |
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  ### Framework versions
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