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xlnet-large-cased-detect-dep

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End of training

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  1. README.md +10 -11
  2. pytorch_model.bin +1 -1
  3. runs/Jul19_12-48-05_8927056ca2ae/events.out.tfevents.1689771645.8927056ca2ae.78.0 +2 -2
README.md CHANGED
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  ---
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  license: mit
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- base_model: xlnet-large-cased
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  tags:
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  - generated_from_trainer
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  metrics:
@@ -18,9 +17,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5283
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- - Accuracy: 0.753
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- - F1: 0.8237
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-06
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | 0.6329 | 1.0 | 1502 | 0.5418 | 0.731 | 0.8125 |
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- | 0.6137 | 2.0 | 3004 | 0.5283 | 0.753 | 0.8237 |
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  ### Framework versions
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- - Transformers 4.31.0
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- - Pytorch 2.0.1+cu118
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- - Datasets 2.13.1
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  - Tokenizers 0.13.3
 
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  ---
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  license: mit
 
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  tags:
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  - generated_from_trainer
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  metrics:
 
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  This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5463
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+ - Accuracy: 0.743
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+ - F1: 0.8152
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-06
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | 0.6479 | 1.0 | 751 | 0.5937 | 0.7 | 0.7981 |
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+ | 0.5988 | 2.0 | 1502 | 0.5463 | 0.743 | 0.8152 |
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  ### Framework versions
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+ - Transformers 4.30.1
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+ - Pytorch 2.0.0
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+ - Datasets 2.1.0
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  - Tokenizers 0.13.3
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