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  ---
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- license: mit
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- tags:
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- - generated_from_trainer
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  datasets:
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- - imdb
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  metrics:
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  - accuracy
 
 
 
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  model-index:
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  - name: gpt2-imdb-sentiment-classifier
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  results:
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  - name: Accuracy
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  type: accuracy
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  value: 0.9394
 
 
 
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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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  # gpt2-imdb-sentiment-classifier
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- This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the imdb dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1703
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- - Accuracy: 0.9394
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-
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- ## Model description
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-
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- More information needed
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  ## Intended uses & limitations
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-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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- - num_epochs: 1
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-
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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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- | 0.1967 | 1.0 | 1563 | 0.1703 | 0.9394 |
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-
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  ### Framework versions
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- - Transformers 4.18.0
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- - Pytorch 1.13.1+cu117
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- - Datasets 2.9.0
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- - Tokenizers 0.12.1
 
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  ---
 
 
 
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  datasets:
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+ - seamew/ChnSentiCorp
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  metrics:
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  - accuracy
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+ - precision
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+ - f1
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+ - recall
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  model-index:
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  - name: gpt2-imdb-sentiment-classifier
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  results:
 
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  - name: Accuracy
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  type: accuracy
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  value: 0.9394
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+ language:
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+ - zh
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+ pipeline_tag: text-classification
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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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  # gpt2-imdb-sentiment-classifier
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+ This model is a fine-tuned version of [hfl/rbt6](https://huggingface.co/hfl/rbt6) on the ChnSentiCorp dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.294600
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+ - Accuracy: 0.933884
 
 
 
 
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  ## Intended uses & limitations
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  ### Training hyperparameters
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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: 4
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+ - eval_batch_size: 4
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+ - weight_decay=1e-2
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+ - num_train_epochs=3
 
 
 
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  ### Training results
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+ Epoch Training Loss Validation Loss Accuracy F1 Precision Recall
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+ 1 0.359700 0.306089 0.924242 0.926230 0.918699 0.933884
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+ 2 0.200600 0.295512 0.942761 0.943615 0.946755 0.940496
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+ 3 0.105600 0.294600 0.941919 0.942452 0.951178 0.933884
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  ### Framework versions
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+ - Pytorch 2.0.0
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+ - Python 3.9.12