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--- |
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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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- generated_from_trainer |
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metrics: |
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- f1 |
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model-index: |
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- name: movie-review-classifier |
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results: [] |
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--- |
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# movie-review-classifier |
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This model classifies (text) movie reviews as either a 1 (*i.e.,* thumbs-up) or a 0 (*i.e.,* a thumbs-down). |
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## Model description |
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This model is a version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) that was fine-tuned on the [IMDB movie-review dataset](https://huggingface.co/datasets/stanfordnlp/imdb). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2743 |
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- F1: 0.9327 |
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## Intended uses & limitations |
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Training this model was completed as part of a project from a data science bootcamp. It is intended to be used perhaps by students and/or hobbyists. |
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## Training and evaluation data |
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This model was trained on the [IMDB movie-review dataset](https://huggingface.co/datasets/stanfordnlp/imdb), a set of highly polarized (*i.e.,* clearly positive or negative) movie reviews. The dataset contains 25k labelled train samples, 25k labelled test samples, and 50k unlabelled samples. |
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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: 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: 3 |
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- weight_decay: 0.1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.2258 | 1.0 | 1563 | 0.2161 | 0.9122 | |
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| 0.1486 | 2.0 | 3126 | 0.2291 | 0.9306 | |
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| 0.0916 | 3.0 | 4689 | 0.2743 | 0.9327 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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