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--- |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: finetuning-sentiment-model-roberta |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: imdb |
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type: imdb |
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config: plain_text |
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split: test |
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args: plain_text |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.93 |
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- name: F1 |
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type: f1 |
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value: 0.9297658862876254 |
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- name: Precision |
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type: precision |
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value: 0.9328859060402684 |
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- name: Recall |
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type: recall |
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value: 0.9266666666666666 |
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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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should probably proofread and complete it, then remove this comment. --> |
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# finetuning-sentiment-model-roberta |
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This model was trained from scratch on the imdb dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2171 |
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- Accuracy: 0.93 |
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- F1: 0.9298 |
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- Precision: 0.9329 |
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- Recall: 0.9267 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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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: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 64 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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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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| 0.144 | 0.98 | 46 | 0.2348 | 0.91 | 0.9132 | 0.8820 | 0.9467 | |
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| 0.0957 | 1.98 | 93 | 0.2171 | 0.93 | 0.9298 | 0.9329 | 0.9267 | |
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| 0.08 | 2.94 | 138 | 0.2554 | 0.9133 | 0.9167 | 0.8827 | 0.9533 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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