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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: distilbert-base-uncased |
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tags: |
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- classification |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: imdb-distilbert |
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results: [] |
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--- |
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# imdb-distilbert |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased). |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4670 |
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- Accuracy: 0.8528 |
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## Model description |
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This model, named imdb-distilbert, is fine-tuned from the distilbert-base-uncased checkpoint on the IMDB movie review dataset for the sentiment classification task. |
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It's designed to predict whether a movie review is positive or negative based on the textual content of the review. |
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This model can be used to automatically classify new movie reviews into positive or negative categories. |
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## Intended uses & limitations |
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While effective within its trained domain, the model may exhibit reduced performance on text that diverges significantly from movie reviews in style or content, |
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such as professional critiques or reviews from non-English sources translated to English. The training dataset predominantly contains informal consumer reviews, |
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which might limit the model's effectiveness with formally written text. |
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## Training and evaluation data |
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The model was trained on the IMDB dataset, which contains 50,000 movie reviews split evenly into 25,000 training and 25,000 testing datasets. |
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Each entry is labeled as either 0 (negative) or 1 (positive). |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4670 |
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- Accuracy: 0.8528 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use 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: 10 |
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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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| 0.3777 | 1.0 | 3125 | 0.4630 | 0.8263 | |
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| 0.2795 | 2.0 | 6250 | 0.4771 | 0.8549 | |
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| 0.1698 | 3.0 | 9375 | 0.5689 | 0.8526 | |
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| 0.1093 | 4.0 | 12500 | 0.9568 | 0.8460 | |
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| 0.0664 | 5.0 | 15625 | 1.0550 | 0.8470 | |
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| 0.0333 | 6.0 | 18750 | 1.1734 | 0.8487 | |
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| 0.0238 | 7.0 | 21875 | 1.1931 | 0.8482 | |
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| 0.0123 | 8.0 | 25000 | 1.2663 | 0.8507 | |
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| 0.0056 | 9.0 | 28125 | 1.3256 | 0.8549 | |
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| 0.0022 | 10.0 | 31250 | 1.4670 | 0.8528 | |
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### Framework versions |
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- Transformers 4.47.0 |
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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 |
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