Datasets:
Search is not available for this dataset
labels class label 2
classes | input_ids listlengths 96 96 | token_type_ids listlengths 96 96 | attention_mask listlengths 96 96 |
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End of preview. Expand in Data Studio
Rotten Tomatoes Cleaned Dataset
Dataset Overview
This dataset is a cleaned and tokenized version of the Rotten Tomatoes sentiment classification dataset.
The dataset is prepared for binary sentiment classification, where each movie review is classified as either negative or positive.
Task Type
Sentiment Classification
Labels
| Label | Meaning |
|---|---|
| 0 | negative |
| 1 | positive |
Dataset Splits
| Split | Number of Rows |
|---|---|
| train | 8,530 |
| validation | 1,066 |
| test | 1,066 |
Processing Steps
The dataset was processed using the following steps:
- Loaded the original Rotten Tomatoes dataset from Hugging Face.
- Cleaned the text data.
- Added useful columns such as text length, word count, and readable label names.
- Tokenized the text using DistilBERT tokenizer.
- Renamed the label column from
labeltolabels. - Removed unnecessary columns for model training.
- Converted the dataset format for PyTorch training.
Tokenizer Information
| Item | Value |
|---|---|
| Tokenizer | distilbert-base-uncased |
| Max Length | 96 |
| Padding | max_length |
| Truncation | True |
Final Columns
The final tokenized dataset contains:
labelsinput_idstoken_type_idsattention_mask
Intended Use
This dataset can be used for training and evaluating a text classification model for movie review sentiment analysis.
Notes
This dataset was prepared as part of an educational NLP workflow using Hugging Face Datasets and Transformers.
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