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my_awesome_model

Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
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xet
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update model card README.md

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  1. README.md +28 -21
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@@ -4,6 +4,7 @@ tags:
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
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  model-index:
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  - name: my_awesome_model
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  results: []
@@ -16,8 +17,9 @@ 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 the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7876
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- - Accuracy: 0.8711
 
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  ## Model description
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@@ -36,33 +38,38 @@ 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: 1e-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: 15
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 167 | 0.3886 | 0.8291 |
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- | No log | 2.0 | 334 | 0.3109 | 0.8711 |
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- | 0.3456 | 3.0 | 501 | 0.3250 | 0.8861 |
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- | 0.3456 | 4.0 | 668 | 0.3831 | 0.8741 |
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- | 0.3456 | 5.0 | 835 | 0.4527 | 0.8816 |
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- | 0.113 | 6.0 | 1002 | 0.5228 | 0.8846 |
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- | 0.113 | 7.0 | 1169 | 0.5790 | 0.8786 |
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- | 0.113 | 8.0 | 1336 | 0.6979 | 0.8651 |
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- | 0.0229 | 9.0 | 1503 | 0.6901 | 0.8771 |
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- | 0.0229 | 10.0 | 1670 | 0.7257 | 0.8696 |
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- | 0.0229 | 11.0 | 1837 | 0.7485 | 0.8726 |
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- | 0.0065 | 12.0 | 2004 | 0.7813 | 0.8696 |
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- | 0.0065 | 13.0 | 2171 | 0.7800 | 0.8696 |
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- | 0.0065 | 14.0 | 2338 | 0.7985 | 0.8636 |
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- | 0.0043 | 15.0 | 2505 | 0.7876 | 0.8711 |
 
 
 
 
 
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  ### Framework versions
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
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+ - f1
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  model-index:
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  - name: my_awesome_model
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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 the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3741
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+ - Accuracy: 0.8411
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+ - F1: 0.8015
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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: 1e-06
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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: 20
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | No log | 1.0 | 167 | 0.6611 | 0.6612 | 0.3722 |
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+ | No log | 2.0 | 334 | 0.5848 | 0.7421 | 0.6325 |
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+ | 0.6188 | 3.0 | 501 | 0.5176 | 0.7601 | 0.7080 |
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+ | 0.6188 | 4.0 | 668 | 0.4783 | 0.7706 | 0.7193 |
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+ | 0.6188 | 5.0 | 835 | 0.4566 | 0.7841 | 0.7273 |
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+ | 0.455 | 6.0 | 1002 | 0.4415 | 0.7946 | 0.7360 |
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+ | 0.455 | 7.0 | 1169 | 0.4250 | 0.8096 | 0.7703 |
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+ | 0.455 | 8.0 | 1336 | 0.4146 | 0.8186 | 0.7713 |
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+ | 0.3881 | 9.0 | 1503 | 0.4069 | 0.8261 | 0.7803 |
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+ | 0.3881 | 10.0 | 1670 | 0.3968 | 0.8321 | 0.7926 |
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+ | 0.3881 | 11.0 | 1837 | 0.3916 | 0.8351 | 0.7948 |
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+ | 0.3493 | 12.0 | 2004 | 0.3865 | 0.8426 | 0.8037 |
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+ | 0.3493 | 13.0 | 2171 | 0.3822 | 0.8426 | 0.8045 |
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+ | 0.3493 | 14.0 | 2338 | 0.3802 | 0.8456 | 0.8068 |
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+ | 0.3239 | 15.0 | 2505 | 0.3769 | 0.8471 | 0.8111 |
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+ | 0.3239 | 16.0 | 2672 | 0.3756 | 0.8441 | 0.8060 |
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+ | 0.3239 | 17.0 | 2839 | 0.3749 | 0.8411 | 0.8015 |
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+ | 0.3104 | 18.0 | 3006 | 0.3742 | 0.8396 | 0.8000 |
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+ | 0.3104 | 19.0 | 3173 | 0.3738 | 0.8396 | 0.8000 |
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+ | 0.3104 | 20.0 | 3340 | 0.3741 | 0.8411 | 0.8015 |
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  ### Framework versions