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BERTModified-finetuned-wikitext-test

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  license: apache-2.0
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  tags:
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  - generated_from_trainer
 
 
 
 
 
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  model-index:
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  - name: BERTModified-finetuned-wikitext-test
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  results: []
@@ -14,7 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 5.8018
 
 
 
 
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  ## Model description
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@@ -33,23 +42,68 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 2e-05
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- - train_batch_size: 16
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- - eval_batch_size: 16
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 5
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss |
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- |:-------------:|:-----:|:----:|:---------------:|
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- | 6.8778 | 1.0 | 1096 | 6.3188 |
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- | 5.9673 | 2.0 | 2192 | 6.0563 |
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- | 5.6729 | 3.0 | 3288 | 5.9121 |
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- | 5.5048 | 4.0 | 4384 | 5.8427 |
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- | 5.4082 | 5.0 | 5480 | 5.8018 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  license: apache-2.0
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  tags:
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  - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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  model-index:
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  - name: BERTModified-finetuned-wikitext-test
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  results: []
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 18.8994
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+ - Precision: 0.25
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+ - Recall: 0.25
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+ - F1: 0.25
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+ - Accuracy: 0.25
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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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+ - learning_rate: 5e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 50
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 19.9877 | 1.0 | 250 | 19.8070 | 0.0385 | 0.0385 | 0.0385 | 0.0385 |
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+ | 15.4776 | 2.0 | 500 | 20.2930 | 0.0577 | 0.0577 | 0.0577 | 0.0577 |
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+ | 13.1238 | 3.0 | 750 | 20.1112 | 0.0769 | 0.0769 | 0.0769 | 0.0769 |
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+ | 11.1387 | 4.0 | 1000 | 19.9105 | 0.0897 | 0.0897 | 0.0897 | 0.0897 |
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+ | 9.5317 | 5.0 | 1250 | 19.9108 | 0.1282 | 0.1282 | 0.1282 | 0.1282 |
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+ | 8.037 | 6.0 | 1500 | 19.6093 | 0.1410 | 0.1410 | 0.1410 | 0.1410 |
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+ | 6.7498 | 7.0 | 1750 | 19.1636 | 0.1474 | 0.1474 | 0.1474 | 0.1474 |
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+ | 5.6472 | 8.0 | 2000 | 19.6709 | 0.1538 | 0.1538 | 0.1538 | 0.1538 |
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+ | 4.6665 | 9.0 | 2250 | 19.2537 | 0.1667 | 0.1667 | 0.1667 | 0.1667 |
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+ | 3.9107 | 10.0 | 2500 | 19.1982 | 0.1474 | 0.1474 | 0.1474 | 0.1474 |
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+ | 3.1874 | 11.0 | 2750 | 18.9938 | 0.1731 | 0.1731 | 0.1731 | 0.1731 |
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+ | 2.5846 | 12.0 | 3000 | 18.7462 | 0.2115 | 0.2115 | 0.2115 | 0.2115 |
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+ | 2.1464 | 13.0 | 3250 | 19.0017 | 0.1667 | 0.1667 | 0.1667 | 0.1667 |
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+ | 1.7521 | 14.0 | 3500 | 18.4513 | 0.1859 | 0.1859 | 0.1859 | 0.1859 |
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+ | 1.4561 | 15.0 | 3750 | 18.7532 | 0.2051 | 0.2051 | 0.2051 | 0.2051 |
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+ | 1.2254 | 16.0 | 4000 | 18.3970 | 0.2179 | 0.2179 | 0.2179 | 0.2179 |
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+ | 1.0416 | 17.0 | 4250 | 18.9764 | 0.1859 | 0.1859 | 0.1859 | 0.1859 |
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+ | 0.8923 | 18.0 | 4500 | 18.3271 | 0.2244 | 0.2244 | 0.2244 | 0.2244 |
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+ | 0.7803 | 19.0 | 4750 | 18.5893 | 0.2436 | 0.2436 | 0.2436 | 0.2436 |
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+ | 0.6839 | 20.0 | 5000 | 18.3505 | 0.2051 | 0.2051 | 0.2051 | 0.2051 |
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+ | 0.6175 | 21.0 | 5250 | 18.6798 | 0.2051 | 0.2051 | 0.2051 | 0.2051 |
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+ | 0.5491 | 22.0 | 5500 | 18.7426 | 0.2115 | 0.2115 | 0.2115 | 0.2115 |
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+ | 0.4952 | 23.0 | 5750 | 18.3955 | 0.2179 | 0.2179 | 0.2179 | 0.2179 |
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+ | 0.4441 | 24.0 | 6000 | 18.5502 | 0.2564 | 0.2564 | 0.2564 | 0.2564 |
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+ | 0.4047 | 25.0 | 6250 | 18.9599 | 0.2244 | 0.2244 | 0.2244 | 0.2244 |
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+ | 0.3768 | 26.0 | 6500 | 18.8141 | 0.2308 | 0.2308 | 0.2308 | 0.2308 |
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+ | 0.3435 | 27.0 | 6750 | 18.9732 | 0.2436 | 0.2436 | 0.2436 | 0.2436 |
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+ | 0.3164 | 28.0 | 7000 | 18.9216 | 0.2372 | 0.2372 | 0.2372 | 0.2372 |
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+ | 0.2954 | 29.0 | 7250 | 18.6152 | 0.1987 | 0.1987 | 0.1987 | 0.1987 |
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+ | 0.2736 | 30.0 | 7500 | 18.6001 | 0.25 | 0.25 | 0.25 | 0.25 |
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+ | 0.2491 | 31.0 | 7750 | 19.1374 | 0.2436 | 0.2436 | 0.2436 | 0.2436 |
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+ | 0.2359 | 32.0 | 8000 | 18.8624 | 0.25 | 0.25 | 0.25 | 0.25 |
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+ | 0.2222 | 33.0 | 8250 | 18.3201 | 0.2308 | 0.2308 | 0.2308 | 0.2308 |
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+ | 0.212 | 34.0 | 8500 | 18.7708 | 0.2179 | 0.2179 | 0.2179 | 0.2179 |
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+ | 0.1864 | 35.0 | 8750 | 18.8994 | 0.2372 | 0.2372 | 0.2372 | 0.2372 |
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+ | 0.1771 | 36.0 | 9000 | 18.3130 | 0.2308 | 0.2308 | 0.2308 | 0.2308 |
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+ | 0.1703 | 37.0 | 9250 | 18.6183 | 0.2436 | 0.2436 | 0.2436 | 0.2436 |
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+ | 0.1554 | 38.0 | 9500 | 18.8593 | 0.2372 | 0.2372 | 0.2372 | 0.2372 |
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+ | 0.1469 | 39.0 | 9750 | 18.8936 | 0.2628 | 0.2628 | 0.2628 | 0.2628 |
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+ | 0.1407 | 40.0 | 10000 | 18.9002 | 0.2372 | 0.2372 | 0.2372 | 0.2372 |
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+ | 0.1328 | 41.0 | 10250 | 19.1827 | 0.2564 | 0.2564 | 0.2564 | 0.2564 |
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+ | 0.1297 | 42.0 | 10500 | 18.5465 | 0.25 | 0.25 | 0.25 | 0.25 |
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+ | 0.1226 | 43.0 | 10750 | 18.9125 | 0.2308 | 0.2308 | 0.2308 | 0.2308 |
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+ | 0.1218 | 44.0 | 11000 | 19.0831 | 0.2308 | 0.2308 | 0.2308 | 0.2308 |
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+ | 0.1136 | 45.0 | 11250 | 18.7969 | 0.2372 | 0.2372 | 0.2372 | 0.2372 |
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+ | 0.1075 | 46.0 | 11500 | 18.7629 | 0.25 | 0.25 | 0.25 | 0.25 |
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+ | 0.1044 | 47.0 | 11750 | 18.9700 | 0.2115 | 0.2115 | 0.2115 | 0.2115 |
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+ | 0.1042 | 48.0 | 12000 | 18.7211 | 0.2628 | 0.2628 | 0.2628 | 0.2628 |
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+ | 0.1008 | 49.0 | 12250 | 18.9104 | 0.2244 | 0.2244 | 0.2244 | 0.2244 |
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+ | 0.1014 | 50.0 | 12500 | 18.7892 | 0.25 | 0.25 | 0.25 | 0.25 |
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  ### Framework versions