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Datasets:
scaredmeow
/
shopee-reviews-tl-binary

Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Languages:
Tagalog
Size:
10K - 100K
Tags:
reviews
shopee
DOI:
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
shopee-reviews-tl-binary
File size: 2,818 Bytes 
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---
license: odc-by
task_categories:
- text-classification
language:
- tl
tags:
- reviews
- shopee
size_categories:
- 10K<n<100K
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': negative
          '1': positive
---

# Dataset Card for Dataset Name

## Dataset Description

- **Homepage:** 
- **Repository:** 
- **Paper:** [Enhancement to Low Resource Text Classification via Sequential Transfer Learning](#)
- **Leaderboard:** 
- **Point of Contact:** [Neil Riego](mailto:neilchristianriego3@gmail.com)

### Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

[More Information Needed]

## Dataset Structure

### Data Instances

A typical data point, comprises of a text and the corresponding label.

An example from the YelpReviewFull test set looks as follows:
```
{
    'label': pos,
    'text': 'Huyyy ang gandaaaaaaaaaaa. Grabe sobrang ganda talaga wala ako masabi. Complete orders pa pinadala sa akin. Buti hindi nabasag kahit walang bubble wrap. Okay na lang din para save mother earth and at least hindi nabasag hehe. Oorder ulit ako ang ganda eh'
}
```

### Data Fields

- 'text': The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes ("").
- 'label': Corresponds to the score associated with the review (between positive and negative).

### Data Splits

The Shopee reviews tl binary dataset is constructed by randomly taking 14000 training samples and 3000 samples for testing and validation for each review star from neg and pos.
In total there are 28000 training samples and 6000 each in validation and testing samples.


## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

[More Information Needed]