--- 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