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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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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: bert-imdb |
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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: 0.93956 |
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- name: F1 |
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type: f1 |
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value: 0.9395537111681099 |
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- name: Precision |
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type: precision |
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value: 0.939743003448315 |
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- name: Recall |
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type: recall |
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value: 0.93956 |
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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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# bert-imdb |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the imdb dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2266 |
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- Accuracy: 0.9396 |
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- F1: 0.9396 |
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- Precision: 0.9397 |
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- Recall: 0.9396 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 9072 |
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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: 2 |
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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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| 0.2223 | 1.0 | 1563 | 0.1898 | 0.9328 | 0.9327 | 0.9331 | 0.9328 | |
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| 0.1161 | 2.0 | 3126 | 0.2266 | 0.9396 | 0.9396 | 0.9397 | 0.9396 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.2 |
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