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bobbyw
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copilot_relex_v1

Text Classification
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deberta-v2
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End of training

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  1. README.md +30 -13
  2. model.safetensors +1 -1
README.md CHANGED
@@ -20,10 +20,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0139
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- - Accuracy: 0.0014
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- - F1: 0.0027
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- - Precision: 0.0014
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  - Recall: 1.0
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  ## Model description
@@ -43,26 +43,43 @@ 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: 3
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | No log | 1.0 | 172 | 0.0372 | 0.0014 | 0.0027 | 0.0014 | 1.0 |
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- | No log | 2.0 | 344 | 0.0159 | 0.0014 | 0.0027 | 0.0014 | 1.0 |
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- | 0.1035 | 3.0 | 516 | 0.0139 | 0.0014 | 0.0027 | 0.0014 | 1.0 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.35.2
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- - Pytorch 2.1.0+cu121
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- - Datasets 2.17.0
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- - Tokenizers 0.15.1
 
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  This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0374
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+ - Accuracy: 0.0069
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+ - F1: 0.0137
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+ - Precision: 0.0069
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  - Recall: 1.0
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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: 0.002
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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 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | No log | 1.0 | 39 | 0.0447 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | No log | 2.0 | 78 | 0.0401 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | No log | 3.0 | 117 | 0.0401 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | No log | 4.0 | 156 | 0.0395 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | No log | 5.0 | 195 | 0.0379 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | No log | 6.0 | 234 | 0.0374 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | No log | 7.0 | 273 | 0.0383 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | No log | 8.0 | 312 | 0.0378 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | No log | 9.0 | 351 | 0.0384 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | No log | 10.0 | 390 | 0.0371 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | No log | 11.0 | 429 | 0.0378 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | No log | 12.0 | 468 | 0.0371 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | 0.037 | 13.0 | 507 | 0.0373 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | 0.037 | 14.0 | 546 | 0.0380 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | 0.037 | 15.0 | 585 | 0.0390 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | 0.037 | 16.0 | 624 | 0.0373 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | 0.037 | 17.0 | 663 | 0.0376 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | 0.037 | 18.0 | 702 | 0.0371 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | 0.037 | 19.0 | 741 | 0.0375 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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+ | 0.037 | 20.0 | 780 | 0.0374 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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  ### Framework versions
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+ - Transformers 4.40.0
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.19.0
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+ - Tokenizers 0.19.1
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