Update README.md

README.md

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+## How to Load the Dataset
+
+This section provides examples of how to load the `Research-EAI/eai-taxonomy-math-w-fm` dataset using different Python libraries and frameworks.
+
+### Using Hugging Face Datasets (Standard Method)
+
+The simplest way to load the dataset is using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the entire dataset
+dataset = load_dataset("Research-EAI/eai-taxonomy-math-w-fm")
+
+# View dataset structure
+print(dataset)
+print(f"Number of examples: {len(dataset['train'])}")
+```
+
+You can also load the dataset in streaming mode to avoid downloading the entire dataset at once:
+
+```python
+from datasets import load_dataset
+
+# Load in streaming mode
+dataset = load_dataset("Research-EAI/eai-taxonomy-math-w-fm", streaming=True)
+data_stream = dataset["train"]
+
+# Iterate through examples
+for example in data_stream.take(5):
+    print(example)
+```
+
+### Using PySpark
+
+For large-scale distributed processing, you can load the dataset using PySpark with the `pyspark_huggingface` library:
+
+```python
+# First install the required library:
+# pip install pyspark_huggingface
+
+import pyspark_huggingface
+from pyspark.sql import SparkSession
+
+# Initialize Spark session
+spark = SparkSession.builder.appName("EAI-Taxonomy-Math").getOrCreate()
+
+# Load the dataset using the "huggingface" data source
+df = spark.read.format("huggingface").load("Research-EAI/eai-taxonomy-math-w-fm")
+
+# Basic dataset exploration
+print(f"Dataset shape: {df.count()} rows, {len(df.columns)} columns")
+df.show(10)
+df.printSchema()
+
+# Load only specific columns for efficiency
+df_subset = (
+    spark.read.format("huggingface")
+    .option("columns", '["column1", "column2"]')  # Replace with actual column names
+    .load("Research-EAI/eai-taxonomy-math-w-fm")
+)
+
+# Run SQL queries on the dataset
+df.createOrReplaceTempView("eai_math_dataset")
+result = spark.sql("""
+    SELECT COUNT(*) as total_examples
+    FROM eai_math_dataset
+""")
+result.show()
+```
+
+### Using Daft
+
+Daft provides a modern DataFrame library optimized for machine learning workloads. You can load the dataset directly from Hugging Face:
+
+```python
+import daft
+
+# Load the entire dataset
+df = daft.read_parquet("hf://datasets/Research-EAI/eai-taxonomy-math-w-fm")
+
+# Basic exploration
+print("Dataset schema:")
+df.schema()
+
+print("First 5 rows:")
+df.show(5)
+```
+
+If you need to access private datasets or use authentication:
+
+```python
+import daft
+import os
+
+# Set your Hugging Face token
+os.environ["HF_TOKEN"] = "your_huggingface_token_here"
+
+# Load with authentication
+df = daft.read_parquet(
+    "hf://datasets/Research-EAI/eai-taxonomy-math-w-fm",
+    hf_token=os.environ["HF_TOKEN"]
+)
+```
+
+### Installation Requirements
+
+Make sure you have the required libraries installed:
+
+```bash
+# For Hugging Face datasets
+pip install datasets
+
+# For PySpark with Hugging Face integration
+pip install pyspark_huggingface
+
+# For Daft
+pip install daft
+```
+
 ## 🎓 Citation
 
 If you use this dataset, please cite our EssentialWeb paper: