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Doowon96
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roberta-base-finetuned-tc

Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
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  1. README.md +16 -16
  2. model.safetensors +1 -1
README.md CHANGED
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  ---
 
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  tags:
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  - generated_from_trainer
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  metrics:
@@ -6,7 +7,6 @@ metrics:
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  - f1
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  - precision
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  - recall
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- base_model: klue/roberta-base
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  model-index:
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  - name: roberta-base-finetuned-tc
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  results: []
@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6339
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- - Accuracy: 0.8293
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- - F1: 0.8259
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- - Precision: 0.8249
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- - Recall: 0.8293
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  ## Model description
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@@ -55,16 +55,16 @@ The following hyperparameters were used during training:
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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 | 16 | 0.6302 | 0.8209 | 0.8055 | 0.8172 | 0.8209 |
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- | No log | 2.0 | 32 | 0.6061 | 0.8113 | 0.8011 | 0.8175 | 0.8113 |
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- | No log | 3.0 | 48 | 0.5938 | 0.8269 | 0.8203 | 0.8254 | 0.8269 |
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- | No log | 4.0 | 64 | 0.5889 | 0.8377 | 0.8311 | 0.8365 | 0.8377 |
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- | No log | 5.0 | 80 | 0.5907 | 0.8425 | 0.8387 | 0.8387 | 0.8425 |
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- | No log | 6.0 | 96 | 0.6237 | 0.8317 | 0.8289 | 0.8296 | 0.8317 |
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- | No log | 7.0 | 112 | 0.6401 | 0.8233 | 0.8209 | 0.8193 | 0.8233 |
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- | No log | 8.0 | 128 | 0.6498 | 0.8245 | 0.8204 | 0.8194 | 0.8245 |
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- | No log | 9.0 | 144 | 0.6413 | 0.8269 | 0.8233 | 0.8223 | 0.8269 |
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- | No log | 10.0 | 160 | 0.6339 | 0.8293 | 0.8259 | 0.8249 | 0.8293 |
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  ### Framework versions
 
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  ---
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+ base_model: klue/roberta-base
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  tags:
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  - generated_from_trainer
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  metrics:
 
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  - f1
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  - precision
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  - recall
 
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  model-index:
11
  - name: roberta-base-finetuned-tc
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  results: []
 
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  This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5739
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+ - Accuracy: 0.8503
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+ - F1: 0.8459
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+ - Precision: 0.8505
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+ - Recall: 0.8503
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  ## Model description
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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 | 18 | 0.9140 | 0.6925 | 0.6174 | 0.6580 | 0.6925 |
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+ | No log | 2.0 | 36 | 0.7353 | 0.7688 | 0.7336 | 0.7220 | 0.7688 |
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+ | No log | 3.0 | 54 | 0.6219 | 0.8126 | 0.7886 | 0.7683 | 0.8126 |
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+ | No log | 4.0 | 72 | 0.5864 | 0.8350 | 0.8256 | 0.8361 | 0.8350 |
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+ | No log | 5.0 | 90 | 0.5614 | 0.8401 | 0.8320 | 0.8400 | 0.8401 |
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+ | No log | 6.0 | 108 | 0.5639 | 0.8452 | 0.8394 | 0.8450 | 0.8452 |
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+ | No log | 7.0 | 126 | 0.5772 | 0.8483 | 0.8424 | 0.8484 | 0.8483 |
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+ | No log | 8.0 | 144 | 0.5684 | 0.8493 | 0.8441 | 0.8492 | 0.8493 |
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+ | No log | 9.0 | 162 | 0.5705 | 0.8473 | 0.8431 | 0.8475 | 0.8473 |
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+ | No log | 10.0 | 180 | 0.5739 | 0.8503 | 0.8459 | 0.8505 | 0.8503 |
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  ### Framework versions
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