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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- rotten_tomatoes |
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metrics: |
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- accuracy |
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model-index: |
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- name: outputs |
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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: rotten_tomatoes |
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type: rotten_tomatoes |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8386491557223265 |
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--- |
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# distilbert_rotten_tomatoes_sentiment_classifier |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7927 |
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- Accuracy: 0.8386 |
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## Model description |
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The goal was to fine-tune a model on the rotten_tomatoes dataset to showcase an end-to-end workflow using the Hugging face library. As such, only the bare minimum of pre-processing was used. |
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## Intended uses & limitations |
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The model will be used as part of a blog post to help others engineers better understand what natural language processing is and how to perform a text classification. |
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## Training and evaluation data |
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The model was evaluated using the accuracy metric that form part of the Hugging Face library. |
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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: 64 |
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- eval_batch_size: 64 |
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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: 5 |
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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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| No log | 1.0 | 134 | 0.5940 | 0.8340 | |
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| No log | 2.0 | 268 | 0.7095 | 0.8227 | |
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| No log | 3.0 | 402 | 0.7276 | 0.8321 | |
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| 0.065 | 4.0 | 536 | 0.7693 | 0.8415 | |
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| 0.065 | 5.0 | 670 | 0.7927 | 0.8386 | |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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