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roberta-base-finetuned-ynat_bench

Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
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End of training

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  1. README.md +12 -20
  2. model.safetensors +1 -1
README.md CHANGED
@@ -7,10 +7,6 @@ metrics:
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  model-index:
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  - name: roberta-base-finetuned-ynat
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  results: []
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- datasets:
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- - klue
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- language:
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- - ko
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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
@@ -18,10 +14,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # roberta-base-finetuned-ynat
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- This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3938
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- - F1: 0.8672
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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: 512
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- - eval_batch_size: 512
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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 Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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- | 0.3162 | 0.56 | 50 | 0.4069 | 0.8610 |
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- | 0.276 | 1.11 | 100 | 0.4176 | 0.8587 |
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- | 0.2706 | 1.67 | 150 | 0.4036 | 0.8631 |
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- | 0.2941 | 2.22 | 200 | 0.4232 | 0.8590 |
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- | 0.2778 | 2.78 | 250 | 0.3994 | 0.8623 |
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- | 0.2575 | 3.33 | 300 | 0.3979 | 0.8628 |
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- | 0.2389 | 3.89 | 350 | 0.4008 | 0.8652 |
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- | 0.2258 | 4.44 | 400 | 0.3950 | 0.8653 |
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- | 0.2097 | 5.0 | 450 | 0.3938 | 0.8672 |
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  ### Framework versions
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- - Transformers 4.37.0
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  - Pytorch 2.1.0+cu121
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  - Datasets 2.16.1
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- - Tokenizers 0.15.0
 
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  model-index:
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  - name: roberta-base-finetuned-ynat
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  results: []
 
 
 
 
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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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  # roberta-base-finetuned-ynat
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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: 0.1554
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+ - F1: 0.9566
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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: 256
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+ - eval_batch_size: 256
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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 Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 0.6942 | 0.91 | 250 | 0.1847 | 0.9464 |
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+ | 0.1606 | 1.82 | 500 | 0.1518 | 0.9530 |
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+ | 0.1071 | 2.74 | 750 | 0.1496 | 0.9550 |
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+ | 0.0752 | 3.65 | 1000 | 0.1554 | 0.9566 |
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+ | 0.0536 | 4.56 | 1250 | 0.1586 | 0.9578 |
 
 
 
 
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  ### Framework versions
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+ - Transformers 4.37.2
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  - Pytorch 2.1.0+cu121
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  - Datasets 2.16.1
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+ - Tokenizers 0.15.1
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