Whitzz/EnglishDatasets

Dataset Overview

Dataset Name: Whitzz/EnglishDatasets
License: MIT
Description:
This dataset consists of 100,000+ English words consisting of many version ranging from small to huge, scraped from multiple sources. It can be used for fine-tuning language models, performing text processing tasks, or for applications like spell-checking, word categorization, and more.

How to Use

To use this dataset in Google Colab or any Python environment, follow these steps:

Step 1: Install the Required Library

The dataset is available through the datasets library by Hugging Face. First, you need to install the library by running the following command:

!pip install datasets

Step 2: Load the Dataset

Once the library is installed, you can proceed to load the "Whitzz/EnglishDatasets" dataset. Here's how you can do it:

from datasets import load_dataset

# Load the Whitzz/EnglishDatasets
dataset = load_dataset('Whitzz/EnglishDatasets')

# Print the dataset to check its structure
print(dataset)

When you run this code, it will load the dataset and print a summary of its structure. The dataset might contain multiple splits, such as "train", "test", and "validation".

Step 3: Print Dataset Entries

To view the actual data, you can print a small portion of the dataset. For instance, you can print the first five entries from the "train" split like this:

# Print the first 5 entries in the 'train' split
print(dataset['train'][:5])

Result: DatasetDict({
    train: Dataset({
        features: ['text'],
        num_rows: 100000
    })
})

Additional Operations You Can Perform:

Here are some other functions you might find useful for exploring or processing the dataset:

1. Filter the Dataset:

You can also filter the dataset based on certain conditions. For example, filtering words that start with a specific letter:

# Filter words starting with 'a'
filtered_data = dataset['train'].filter(lambda example: example['text'].startswith('a'))

# Print the first 5 filtered examples
print(filtered_data[:5])

Use the Dataset for Training:

Once you load and explore the dataset, you can use it to fine-tune language models (e.g., GPT-3, BERT). For instance, you can prepare the data by tokenizing it and then feeding it into a model.

Example (using transformers library for tokenization):

from transformers import AutoTokenizer

# Load a pre-trained tokenizer (e.g., BERT tokenizer)
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')

# Tokenize the dataset (just an example)
def tokenize_function(examples):
    return tokenizer(examples['text'], padding='max_length', truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Now tokenized_datasets is ready for fine-tuning or further processing