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  license: apache-2.0
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  ---
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- # 🔬 EAI-Taxonomy STEM w/ DCLM
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  A high-quality STEM dataset curated from web data using taxonomy-based filtering, containing **100 billion tokens** of science, technology, engineering, and mathematics content.
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  ## 🎯 Dataset Overview
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- This dataset is part of the [**Essential-Web**](https://huggingface.co/datasets/EssentialAI/essential-web) project, which introduces a new paradigm for dataset curation using expressive metadata and simple semantic filters. Unlike traditional STEM datasets that require complex domain-specific pipelines, our approach leverages a 12-category taxonomy to efficiently identify and extract high-quality STEM content.
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  **🧪 EAI-Taxonomy STEM w/ DCLM** (100B tokens): Documents targeting science, engineering, medical, and computer science content that exhibit reasoning, combined with the DCLM classifier to filter for instruction-dense documents.
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  ## How to Load the Dataset
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- This section provides examples of how to load the `EssentialAI/eai-taxonomy-stem-w-dclm` dataset using different Python libraries and frameworks.
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  ### Using Hugging Face Datasets (Standard Method)
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  from datasets import load_dataset
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  # Load the entire dataset
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- dataset = load_dataset("EssentialAI/eai-taxonomy-stem-w-dclm")
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  # View dataset structure
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  print(dataset)
@@ -448,7 +448,7 @@ You can also load the dataset in streaming mode to avoid downloading the entire
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  from datasets import load_dataset
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  # Load in streaming mode
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- dataset = load_dataset("EssentialAI/eai-taxonomy-stem-w-dclm", streaming=True)
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  data_stream = dataset["train"]
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  # Iterate through examples
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  spark = SparkSession.builder.appName("EAI-Taxonomy-STEM-w-DCLM").getOrCreate()
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  # Load the dataset using the "huggingface" data source
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- df = spark.read.format("huggingface").load("EssentialAI/eai-taxonomy-stem-w-dclm")
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  # Basic dataset exploration
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  print(f"Dataset shape: {df.count()} rows, {len(df.columns)} columns")
@@ -482,7 +482,7 @@ df.printSchema()
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  df_subset = (
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  spark.read.format("huggingface")
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  .option("columns", '["column1", "column2"]') # Replace with actual column names
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- .load("EssentialAI/eai-taxonomy-stem-w-dclm")
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  )
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  # Run SQL queries on the dataset
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  import daft
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  # Load the entire dataset
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- df = daft.read_parquet("hf://datasets/EssentialAI/eai-taxonomy-stem-w-dclm")
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  # Basic exploration
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  print("Dataset schema:")
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  from daft.io import IOConfig, HTTPConfig
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  io_config = IOConfig(http=HTTPConfig(bearer_token="your_token"))
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- df = daft.read_parquet("hf://datasets/EssentialAI/eai-taxonomy-stem-w-dclm", io_config=io_config)
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  ```
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  ### Installation Requirements
 
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  license: apache-2.0
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  ---
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+ # 🔬 EAI-Taxonomy STEM w/ DCLM (100B sample)
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  A high-quality STEM dataset curated from web data using taxonomy-based filtering, containing **100 billion tokens** of science, technology, engineering, and mathematics content.
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  ## 🎯 Dataset Overview
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+ This dataset is part of the [**Essential-Web**](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) project, which introduces a new paradigm for dataset curation using expressive metadata and simple semantic filters. Unlike traditional STEM datasets that require complex domain-specific pipelines, our approach leverages a 12-category taxonomy to efficiently identify and extract high-quality STEM content.
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  **🧪 EAI-Taxonomy STEM w/ DCLM** (100B tokens): Documents targeting science, engineering, medical, and computer science content that exhibit reasoning, combined with the DCLM classifier to filter for instruction-dense documents.
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  ## How to Load the Dataset
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+ This section provides examples of how to load the `EssentialAI/eai-taxonomy-stem-w-dclm-100b-sample` dataset using different Python libraries and frameworks.
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  ### Using Hugging Face Datasets (Standard Method)
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  from datasets import load_dataset
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  # Load the entire dataset
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+ dataset = load_dataset("EssentialAI/eai-taxonomy-stem-w-dclm-100b-sample")
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  # View dataset structure
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  print(dataset)
 
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  from datasets import load_dataset
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  # Load in streaming mode
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+ dataset = load_dataset("EssentialAI/eai-taxonomy-stem-w-dclm-100b-sample", streaming=True)
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  data_stream = dataset["train"]
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  # Iterate through examples
 
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  spark = SparkSession.builder.appName("EAI-Taxonomy-STEM-w-DCLM").getOrCreate()
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  # Load the dataset using the "huggingface" data source
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+ df = spark.read.format("huggingface").load("EssentialAI/eai-taxonomy-stem-w-dclm-100b-sample")
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  # Basic dataset exploration
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  print(f"Dataset shape: {df.count()} rows, {len(df.columns)} columns")
 
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  df_subset = (
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  spark.read.format("huggingface")
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  .option("columns", '["column1", "column2"]') # Replace with actual column names
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+ .load("EssentialAI/eai-taxonomy-stem-w-dclm-100b-sample")
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  )
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  # Run SQL queries on the dataset
 
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  import daft
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  # Load the entire dataset
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+ df = daft.read_parquet("hf://datasets/EssentialAI/eai-taxonomy-stem-w-dclm-100b-sample")
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  # Basic exploration
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  print("Dataset schema:")
 
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  from daft.io import IOConfig, HTTPConfig
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  io_config = IOConfig(http=HTTPConfig(bearer_token="your_token"))
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+ df = daft.read_parquet("hf://datasets/EssentialAI/eai-taxonomy-stem-w-dclm-100b-sample", io_config=io_config)
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  ```
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  ### Installation Requirements