| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - rotten_tomatoes | |
| metrics: | |
| - accuracy | |
| base_model: distilbert-base-uncased | |
| model-index: | |
| - name: outputs | |
| results: | |
| - task: | |
| type: text-classification | |
| name: Text Classification | |
| dataset: | |
| name: rotten_tomatoes | |
| type: rotten_tomatoes | |
| config: default | |
| split: train | |
| args: default | |
| metrics: | |
| - type: accuracy | |
| value: 0.8386491557223265 | |
| name: Accuracy | |
| # distilbert_rotten_tomatoes_sentiment_classifier | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.7927 | |
| - Accuracy: 0.8386 | |
| ## Model description | |
| 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. | |
| ## Intended uses & limitations | |
| 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. | |
| ## Training and evaluation data | |
| The model was evaluated using the accuracy metric that form part of the Hugging Face library. | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 64 | |
| - eval_batch_size: 64 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | No log | 1.0 | 134 | 0.5940 | 0.8340 | | |
| | No log | 2.0 | 268 | 0.7095 | 0.8227 | | |
| | No log | 3.0 | 402 | 0.7276 | 0.8321 | | |
| | 0.065 | 4.0 | 536 | 0.7693 | 0.8415 | | |
| | 0.065 | 5.0 | 670 | 0.7927 | 0.8386 | | |
| ### Framework versions | |
| - Transformers 4.21.1 | |
| - Pytorch 1.12.1+cu113 | |
| - Datasets 2.4.0 | |
| - Tokenizers 0.12.1 | |