Update README.md

README.md

@@ -416,7 +416,7 @@ 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 `EssentialAI/eai-taxonomy-math-w-fm` dataset using different Python libraries and frameworks.
 
 ### Using Hugging Face Datasets (Standard Method)
 
@@ -426,7 +426,7 @@ 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/eai-taxonomy-math-w-fm")
 
 # View dataset structure
 print(dataset)
@@ -439,7 +439,7 @@ 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/eai-taxonomy-math-w-fm", streaming=True)
 data_stream = dataset["train"]
 
 # Iterate through examples
@@ -462,7 +462,7 @@ from pyspark.sql import SparkSession
 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")
+df = spark.read.format("huggingface").load("EssentialAI/eai-taxonomy-math-w-fm")
 
 # Basic dataset exploration
 print(f"Dataset shape: {df.count()} rows, {len(df.columns)} columns")
@@ -473,7 +473,7 @@ 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/eai-taxonomy-math-w-fm")
 )
 
 # Run SQL queries on the dataset
@@ -493,7 +493,7 @@ 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/eai-taxonomy-math-w-fm")
 
 # Basic exploration
 print("Dataset schema:")
@@ -507,16 +507,10 @@ If you need to access private datasets or use authentication:
 
 ```python
 import daft
-import os
+from daft.io import IOConfig, HTTPConfig
 
-# 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"]
-)
+io_config = IOConfig(http=HTTPConfig(bearer_token="your_token"))
+df = daft.read_parquet("hf://datasets/EssentialAI/eai-taxonomy-math-w-fm", io_config=io_config)
 ```
 
 ### Installation Requirements