Update README.md

Added introduction to the dataset.

README.md

@@ -34,4 +34,110 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+task_categories:
+- text-generation
+language:
+- en
+splits:
+  - name: train
+    num_bytes: 41746813043
+    num_examples: 2494618
+    download_size: 9359508369
+    dataset_size: 41746813043
+
 ---
+# Processed FineWeb-Edu Dataset
+
+**Dataset Name on Hugging Face**: [PursuitOfDataScience/processed-fineweb-edu](https://huggingface.co/datasets/PursuitOfDataScience/processed-fineweb-edu)
+
+
+## Overview
+This dataset is a processed version of the FineWeb-Edu dataset, intended for language model training and NLP research. 
+It has been tokenized and truncated according to a specified block size (i.e., 2048), preparing it for model pre-training or evaluation with transformer-based language models.
+
+## Source Dataset
+- **Name**: FineWeb-Edu  
+- **Description**: A dataset focused on educational text extracted from the web, designed for language modeling and educational NLP tasks.  
+- **Link**: *https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu*
+- **Version**: CC-MAIN-2024-10
+
+## Processing Steps
+The dataset was processed using the [Hugging Face Datasets library](https://github.com/huggingface/datasets) and a Hugging Face tokenizer. The primary steps include:
+
+1. **Tokenization**: Each `text` sample is encoded using the tokenizer’s `.encode()` method.  
+2. **Truncation**: Token sequences are truncated to a specified `block_size + 1`.  
+3. **Filtering**: Any sample with fewer than `block_size + 1` tokens is removed.  
+4. **Saving**: The processed data is saved to disk using `ds.save_to_disk(processed_dir)`.
+
+Below is the code excerpt used to perform these steps:
+
+```python
+def load_nonstream_data(data_files, hf_tokenizer, block_size, num_proc=128):
+    """
+    Loads the entire dataset in memory either from a cached processed directory
+    or processes it in parallel if not yet cached.
+    Returns a list of token ID sequences.
+    """
+
+    processed_dir = "processed_data/tokenized_data"
+    if os.path.exists(processed_dir):
+        print(f"Loading cached dataset from '{processed_dir}'...")
+        ds = load_from_disk(processed_dir)
+        tokenized_data = ds["token_ids"]
+        return tokenized_data
+
+    print("No cached dataset found. Processing in parallel...")
+
+    ds_dict = load_dataset("arrow", data_files=data_files, streaming=False)
+    if "train" in ds_dict:
+        ds = ds_dict["train"]
+    else:
+        ds = ds_dict
+
+    def tokenize_and_truncate(example):
+        text = example["text"] if "text" in example else ""
+        token_ids = hf_tokenizer.encode(text)
+        if len(token_ids) < block_size + 1:
+            return {"token_ids": None}
+        token_ids = token_ids[:block_size+1]
+        return {"token_ids": token_ids}
+
+    ds = ds.map(
+        tokenize_and_truncate,
+        batched=False,
+        num_proc=num_proc
+    )
+    ds = ds.filter(lambda ex: ex["token_ids"] is not None, num_proc=num_proc)
+
+    if "text" in ds.column_names:
+        ds = ds.remove_columns(["text"])
+
+    os.makedirs(os.path.dirname(processed_dir), exist_ok=True)
+    ds.save_to_disk(processed_dir)
+    print(f"Processed dataset saved to '{processed_dir}'.")
+
+    tokenized_data = ds["token_ids"]
+    return tokenized_data
+```
+
+## Dataset Structure
+- **Columns**:  
+  - `token_ids`: A list of token IDs representing a truncated text segment.
+
+- **Splits**:  
+  - This dataset is provided as a single split named `train`.
+
+## Intended Use & Applications
+- **Language Modeling**: Suitable for GPT-style or other auto-regressive models, focusing on educational text.  
+- **Fine-Tuning**: Can be used to fine-tune existing models on educational text.  
+- **Research**: Useful for experimentation in NLP tasks such as text generation.
+
+## How to Load
+You can load this dataset directly from Hugging Face using the `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("PursuitOfDataScience/processed-fineweb-edu")
+print(dataset)
+```