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--- |
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language: |
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- en |
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--- |
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# **Dataset Overview** |
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**Dataset Name**: Whitzz/EnglishDatasets |
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**License**: MIT |
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**Description**: |
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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. |
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# **How to Use** |
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To use this dataset in Google Colab or any Python environment, follow these steps: |
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# **Step 1: Install the Required Library** |
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The dataset is available through the `datasets` library by Hugging Face. First, you need to install the library by running the following command: |
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``` |
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!pip install datasets |
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``` |
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# Step 2: Load the Dataset |
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Once the library is installed, you can proceed to load the "Whitzz/EnglishDatasets" dataset. Here's how you can do it: |
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``` |
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from datasets import load_dataset |
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# Load the Whitzz/EnglishDatasets |
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dataset = load_dataset('Whitzz/EnglishDatasets') |
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# Print the dataset to check its structure |
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print(dataset) |
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``` |
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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". |
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# Step 3: Print Dataset Entries |
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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: |
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``` |
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# Print the first 5 entries in the 'train' split |
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print(dataset['train'][:5]) |
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``` |
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``` |
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Result: DatasetDict({ |
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train: Dataset({ |
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features: ['text'], |
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num_rows: 100000 |
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}) |
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}) |
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``` |
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# Additional Operations You Can Perform: |
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Here are some other functions you might find useful for exploring or processing the dataset: |
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# 1. Filter the Dataset: |
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You can also filter the dataset based on certain conditions. For example, filtering words that start with a specific letter: |
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``` |
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# Filter words starting with 'a' |
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filtered_data = dataset['train'].filter(lambda example: example['text'].startswith('a')) |
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# Print the first 5 filtered examples |
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print(filtered_data[:5]) |
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``` |
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# Use the Dataset for Training: |
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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. |
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Example (using transformers library for tokenization): |
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``` |
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from transformers import AutoTokenizer |
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# Load a pre-trained tokenizer (e.g., BERT tokenizer) |
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tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') |
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# Tokenize the dataset (just an example) |
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def tokenize_function(examples): |
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return tokenizer(examples['text'], padding='max_length', truncation=True) |
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tokenized_datasets = dataset.map(tokenize_function, batched=True) |
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# Now tokenized_datasets is ready for fine-tuning or further processing |
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``` |
