Update README.md

README.md

@@ -414,7 +414,7 @@ Domain and content type classification probabilities:
 
 ## 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.
+This section provides examples of how to load the `Research-EAI/essential-web-1t-sample-fdc-partitioned` dataset using different Python libraries and frameworks.
 
 ### Using Hugging Face Datasets (Standard Method)
 
@@ -424,7 +424,7 @@ The simplest way to load the dataset is using the Hugging Face `datasets` librar
 from datasets import load_dataset
 
 # Load the entire dataset
-dataset = load_dataset("Research-EAI/eai-taxonomy-math-w-fm")
+dataset = load_dataset("EssentialAI/essential-web-1t-sample-fdc-partitioned")
 
 # View dataset structure
 print(dataset)
@@ -437,7 +437,7 @@ You can also load the dataset in streaming mode to avoid downloading the entire
 from datasets import load_dataset
 
 # Load in streaming mode
-dataset = load_dataset("Research-EAI/eai-taxonomy-math-w-fm", streaming=True)
+dataset = load_dataset("EssentialAI/essential-web-1t-sample-fdc-partitioned", streaming=True)
 data_stream = dataset["train"]
 
 # Iterate through examples
@@ -457,10 +457,10 @@ import pyspark_huggingface
 from pyspark.sql import SparkSession
 
 # Initialize Spark session
-spark = SparkSession.builder.appName("EAI-Taxonomy-Math").getOrCreate()
+spark = SparkSession.builder.appName("EAI-Taxonomy-Web-1T-Sample-FDC-Partitioned").getOrCreate()
 
 # Load the dataset using the "huggingface" data source
-df = spark.read.format("huggingface").load("Research-EAI/eai-taxonomy-math-w-fm")
+df = spark.read.format("huggingface").load("EssentialAI/essential-web-1t-sample-fdc-partitioned")
 
 # Basic dataset exploration
 print(f"Dataset shape: {df.count()} rows, {len(df.columns)} columns")
@@ -471,14 +471,14 @@ df.printSchema()
 df_subset = (
     spark.read.format("huggingface")
     .option("columns", '["column1", "column2"]')  # Replace with actual column names
-    .load("Research-EAI/eai-taxonomy-math-w-fm")
+    .load("EssentialAI/essential-web-1t-sample-fdc-partitioned")
 )
 
 # Run SQL queries on the dataset
-df.createOrReplaceTempView("eai_math_dataset")
+df.createOrReplaceTempView("eai_web_1t_sample_fdc_partitioned_dataset")
 result = spark.sql("""
     SELECT COUNT(*) as total_examples
-    FROM eai_math_dataset
+    FROM eai_web_1t_sample_fdc_partitioned_dataset
 """)
 result.show()
 ```
@@ -491,7 +491,7 @@ Daft provides a modern DataFrame library optimized for machine learning workload
 import daft
 
 # Load the entire dataset
-df = daft.read_parquet("hf://datasets/Research-EAI/eai-taxonomy-math-w-fm")
+df = daft.read_parquet("hf://datasets/EssentialAI/essential-web-1t-sample-fdc-partitioned")
 
 # Basic exploration
 print("Dataset schema:")
@@ -512,7 +512,7 @@ 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://datasets/EssentialAI/essential-web-1t-sample-fdc-partitioned",
     hf_token=os.environ["HF_TOKEN"]
 )
 ```