Update README.md

README.md

@@ -422,7 +422,7 @@ Domain and content type classification probabilities:
 
 ## How to Load the Dataset
 
-This section provides examples of how to load the `EssentialAI/eai-taxonomy-medical-w-dclm` dataset using different Python libraries and frameworks.
+This section provides examples of how to load the `EssentialAI/eai-taxonomy-med-w-dclm-100b-sample` dataset using different Python libraries and frameworks.
 
 ### Using Hugging Face Datasets (Standard Method)
 
@@ -432,7 +432,7 @@ 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("EssentialAI/eai-taxonomy-medical-w-dclm")
+dataset = load_dataset("EssentialAI/eai-taxonomy-med-w-dclm-100b-sample")
 
 # View dataset structure
 print(dataset)
@@ -445,7 +445,7 @@ 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("EssentialAI/eai-taxonomy-medical-w-dclm", streaming=True)
+dataset = load_dataset("EssentialAI/eai-taxonomy-med-w-dclm-100b-sample", streaming=True)
 data_stream = dataset["train"]
 
 # Iterate through examples
@@ -465,10 +465,10 @@ import pyspark_huggingface
 from pyspark.sql import SparkSession
 
 # Initialize Spark session
-spark = SparkSession.builder.appName("EAI-Taxonomy-Medical-w-DCLM").getOrCreate()
+spark = SparkSession.builder.appName("EAI-Taxonomy-Med-w-DCLM").getOrCreate()
 
 # Load the dataset using the "huggingface" data source
-df = spark.read.format("huggingface").load("EssentialAI/eai-taxonomy-medical-w-dclm")
+df = spark.read.format("huggingface").load("EssentialAI/eai-taxonomy-med-w-dclm-100b-sample")
 
 # Basic dataset exploration
 print(f"Dataset shape: {df.count()} rows, {len(df.columns)} columns")
@@ -479,14 +479,14 @@ df.printSchema()
 df_subset = (
     spark.read.format("huggingface")
     .option("columns", '["column1", "column2"]')  # Replace with actual column names
-    .load("EssentialAI/eai-taxonomy-medical-w-dclm")
+    .load("EssentialAI/eai-taxonomy-med-w-dclm-100b-sample")
 )
 
 # Run SQL queries on the dataset
-df.createOrReplaceTempView("eai_taxonomy_medical_w_dclm_dataset")
+df.createOrReplaceTempView("eai_taxonomy_med_w_dclm_dataset")
 result = spark.sql("""
     SELECT COUNT(*) as total_examples
-    FROM eai_taxonomy_medical_w_dclm_dataset
+    FROM eai_taxonomy_med_w_dclm_dataset
 """)
 result.show()
 ```
@@ -499,7 +499,7 @@ Daft provides a modern DataFrame library optimized for machine learning workload
 import daft
 
 # Load the entire dataset
-df = daft.read_parquet("hf://datasets/EssentialAI/eai-taxonomy-medical-w-dclm")
+df = daft.read_parquet("hf://datasets/EssentialAI/eai-taxonomy-med-w-dclm-100b-sample")
 
 # Basic exploration
 print("Dataset schema:")
@@ -516,7 +516,7 @@ import daft
 from daft.io import IOConfig, HTTPConfig
 
 io_config = IOConfig(http=HTTPConfig(bearer_token="your_token"))
-df = daft.read_parquet("hf://datasets/EssentialAI/eai-taxonomy-medical-w-dclm", io_config=io_config)
+df = daft.read_parquet("hf://datasets/EssentialAI/eai-taxonomy-med-w-dclm-100b-sample", io_config=io_config)
 ```
 
 ### Installation Requirements