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deberta_textclassification

Text Classification
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Safetensors
deberta
Generated from Trainer
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xet
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duynhatran commited on
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End of training

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  1. README.md +23 -13
  2. model.safetensors +1 -1
README.md CHANGED
@@ -6,7 +6,6 @@ tags:
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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: deberta_textclassification
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  results: []
@@ -19,9 +18,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5291
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- - Accuracy: 0.9043
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- - F1: 0.9320
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  ## Model description
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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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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | 0.4082 | 1.0 | 760 | 0.3256 | 0.8720 | 0.9050 |
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- | 0.2193 | 2.0 | 1520 | 0.2805 | 0.8967 | 0.9259 |
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- | 0.1571 | 3.0 | 2280 | 0.3280 | 0.9089 | 0.9357 |
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- | 0.0947 | 4.0 | 3040 | 0.4283 | 0.9112 | 0.9368 |
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- | 0.0489 | 5.0 | 3800 | 0.5291 | 0.9043 | 0.9320 |
 
 
 
 
 
 
 
 
 
 
 
 
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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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  model-index:
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  - name: deberta_textclassification
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  results: []
 
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  This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3436
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+ - Accuracy: 0.9520
 
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  ## Model description
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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: 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 results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|
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+ | 0.3919 | 0.2924 | 500 | 0.2821 | 0.9184 |
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+ | 0.274 | 0.5848 | 1000 | 0.2795 | 0.9362 |
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+ | 0.2552 | 0.8772 | 1500 | 0.2469 | 0.9355 |
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+ | 0.2148 | 1.1696 | 2000 | 0.3214 | 0.9421 |
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+ | 0.1876 | 1.4620 | 2500 | 0.2636 | 0.9382 |
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+ | 0.1505 | 1.7544 | 3000 | 0.2323 | 0.9467 |
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+ | 0.1411 | 2.0468 | 3500 | 0.3445 | 0.9395 |
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+ | 0.0676 | 2.3392 | 4000 | 0.3280 | 0.9414 |
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+ | 0.1089 | 2.6316 | 4500 | 0.4225 | 0.9270 |
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+ | 0.0888 | 2.9240 | 5000 | 0.2458 | 0.9520 |
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+ | 0.0544 | 3.2164 | 5500 | 0.2877 | 0.9539 |
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+ | 0.0366 | 3.5088 | 6000 | 0.3010 | 0.9553 |
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+ | 0.0322 | 3.8012 | 6500 | 0.3508 | 0.9474 |
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+ | 0.0313 | 4.0936 | 7000 | 0.3302 | 0.9520 |
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+ | 0.0191 | 4.3860 | 7500 | 0.3527 | 0.9493 |
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+ | 0.0118 | 4.6784 | 8000 | 0.3378 | 0.9513 |
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+ | 0.0189 | 4.9708 | 8500 | 0.3436 | 0.9520 |
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  ### Framework versions
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