| # **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 | |
| ``` |