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solikang/koelectra-small-v3-nsmc

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  1. README.md +12 -14
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -24,16 +24,16 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8869565217391304
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  - name: F1
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  type: f1
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- value: 0.8338208775796434
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  - name: Precision
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  type: precision
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- value: 0.7866918714555766
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  - name: Recall
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  type: recall
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- value: 0.8869565217391304
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [monologg/koelectra-small-v3-discriminator](https://huggingface.co/monologg/koelectra-small-v3-discriminator) on the generator dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.2041
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- - Accuracy: 0.8870
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- - F1: 0.8338
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- - Precision: 0.7867
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- - Recall: 0.8870
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  ## Model description
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@@ -76,11 +76,9 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
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- |:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|:---------:|:------:|
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- | No log | 1.0 | 12 | 0.8280 | 0.7500 | 1.4560 | 0.6855 | 0.8280 |
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- | No log | 2.0 | 24 | 0.9140 | 0.8729 | 1.2494 | 0.8354 | 0.9140 |
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- | No log | 1.0 | 29 | 0.9304 | 0.8969 | 1.1763 | 0.8657 | 0.9304 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9173913043478261
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  - name: F1
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  type: f1
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+ value: 0.8778665089224095
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  - name: Precision
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  type: precision
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+ value: 0.8416068052930057
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  - name: Recall
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  type: recall
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+ value: 0.9173913043478261
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [monologg/koelectra-small-v3-discriminator](https://huggingface.co/monologg/koelectra-small-v3-discriminator) on the generator dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2609
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+ - Accuracy: 0.9174
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+ - F1: 0.8779
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+ - Precision: 0.8416
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+ - Recall: 0.9174
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  ## Model description
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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 | 29 | 1.2609 | 0.9174 | 0.8779 | 0.8416 | 0.9174 |
 
 
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  ### Framework versions
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