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xlm-roberta-text-cls-peft-prompt-tuning

PEFT
Safetensors
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  1. README.md +16 -11
  2. adapter_model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [sgkinc/xlm-roberta-text-classification](https://huggingface.co/sgkinc/xlm-roberta-text-classification) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1812
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- - Accuracy: 0.7109
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  ## Model description
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@@ -37,7 +37,7 @@ 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: 3e-05
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  - train_batch_size: 16
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  - eval_batch_size: 8
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  - seed: 42
@@ -45,17 +45,22 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 32
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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: 5.0
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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 | 108 | 1.6224 | 0.6472 |
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- | No log | 2.0 | 216 | 1.3325 | 0.6605 |
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- | No log | 3.0 | 324 | 1.2315 | 0.6923 |
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- | No log | 4.0 | 432 | 1.1934 | 0.7109 |
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- | 1.2712 | 5.0 | 540 | 1.1812 | 0.7109 |
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [sgkinc/xlm-roberta-text-classification](https://huggingface.co/sgkinc/xlm-roberta-text-classification) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7050
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+ - Accuracy: 0.7294
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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: 0.0001
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  - train_batch_size: 16
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  - eval_batch_size: 8
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  - seed: 42
 
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  - total_train_batch_size: 32
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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: 10.0
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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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+ | 1.7278 | 0.9992 | 594 | 1.2574 | 0.5517 |
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+ | 1.3236 | 2.0 | 1189 | 0.9894 | 0.6393 |
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+ | 1.1827 | 2.9992 | 1783 | 0.8649 | 0.6897 |
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+ | 1.1147 | 4.0 | 2378 | 0.8593 | 0.6897 |
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+ | 1.0552 | 4.9992 | 2972 | 0.7745 | 0.6976 |
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+ | 1.0143 | 6.0 | 3567 | 0.7181 | 0.7135 |
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+ | 0.9872 | 6.9992 | 4161 | 0.7037 | 0.7507 |
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+ | 1.002 | 8.0 | 4756 | 0.7111 | 0.7347 |
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+ | 0.9816 | 8.9992 | 5350 | 0.6931 | 0.7241 |
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+ | 0.9602 | 9.9916 | 5940 | 0.7050 | 0.7294 |
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  ### Framework versions
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