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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google-bert/bert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- recall |
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- precision |
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model-index: |
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- name: bert_imdb |
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results: [] |
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datasets: |
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- stanfordnlp/imdb |
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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 [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on imdb dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3119 |
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- Accuracy: 0.9403 |
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- Recall: 0.9430 |
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- Precision: 0.9379 |
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To acccess my finetuning tutorial you can check the following [repository](https://github.com/GoktugGuvercin/Text-Classification). |
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## Comparison with SOTA: |
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- DistilBERT 66M: 92.82 |
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- BERT-base + ITPT: 95.63 |
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- BERT-large: 95.49 |
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Reference: [Paperswithcode](https://paperswithcode.com/sota/sentiment-analysis-on-imdb) |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:| |
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| 0.2099 | 1.0 | 1563 | 0.2456 | 0.9102 | 0.8481 | 0.9683 | |
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| 0.1379 | 2.0 | 3126 | 0.2443 | 0.9274 | 0.8911 | 0.9608 | |
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| 0.0752 | 3.0 | 4689 | 0.2845 | 0.9391 | 0.9509 | 0.9290 | |
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| 0.0352 | 4.0 | 6252 | 0.3119 | 0.9403 | 0.9430 | 0.9379 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
