Datasets:

sphita
/

intel-community

license: gpl-3.0
task_categories:
  - question-answering
  - text-generation
language:
  - en
tags:
  - hardware
  - troubleshooting
  - intel
  - enterprise
  - tech-support
  - discourse
  - liql
size_categories:
  - 100K<n<1M
homepage: https://github.com/sphita/intel-community

💻 Intel WebCorpus Forums (Enterprise Hardware Q&A)

This dataset is a massive, high-fidelity archive of 176,873 technical troubleshooting discussions (containing 941,391 individual messages and 152.8M+ cl100k_base conversational text tokens) scraped from the official Intel Community Forums.

It has been meticulously engineered for Large Language Model (LLM) training. Instead of a raw, messy dump of isolated posts, the data has been reconstructed into chronological conversation threads, noise-filtered, deduplicated, and packed into optimized Snappy-compressed Parquet files.

📊 Dataset Summary

Token volume was calculated using OpenAI's tiktoken (cl100k_base encoding).

Metric	Count
GitHub Repository	`sphita/intel-community`
Total Discussions (Threads)	176,873
Total Unique Messages	941,391
Total Conversational Text Tokens (`cl100k_base`)	152,846,496
Total Structured Tokens (`cl100k_base`)	211,862,442
Average Messages per Thread	5.3
Threads with Accepted Solutions	20,967 (11.9%)
Total Parquet Chunks	1,777
Total Size (Compressed)	~361 MB

🛠️ Data Engineering & Ingestion Architecture

The dataset pipeline and crawler source code are publicly maintained at sphita/intel-community.

The raw data pipeline operates in two distinct phases:

Historical Corpus (Khoros 2.0 API): The bulk archive was extracted using a custom Rust async crawler with LiQL query pagination, rate limiting, and exponential backoff.
Live Delta Sync (Discourse API Migration): On October 13, 2026, Intel migrated community.intel.com from Lithium (Khoros) to Discourse, permanently deprecating the Khoros /api/2.0/search endpoint (HTTP 404). The delta sync engine (delta_sync.rs) was rewritten and verified to consume Discourse's cursor-based /posts.json endpoint, normalizing posts (cooked, post_number, username, reactions) to preserve 100% schema parity with the historical archive.

Core Pipeline Steps:

Thread Reconstruction: Over 940,000 flat messages were grouped by their topic_id to reconstruct the exact back-and-forth conversational flow of human troubleshooting.
Noise Reduction: "Dead-weight" messages (under 5 words, e.g., "bump", "+1", "thanks") were automatically filtered out of the replies to increase the signal-to-noise ratio.
Deduplication: Overlapping chunks were strictly deduplicated by msg_id to ensure absolute uniqueness.
HTML Sanitization: Raw HTML bodies were stripped into clean text.

🗂️ Top Hardware Categories (Boards)

The discussions cover deep technical support for Intel's entire hardware and software ecosystem. The top 10 categories are:

Board / Category	Discussions
`fortran-compiler`	28,488
`graphics`	23,775
`processors`	17,468
`software-archive`	16,556
`wireless`	9,030
`oneapi-math-kernel-library`	7,201
`integrated-performance-primitives`	6,677
`distribution-openvino-toolkit`	6,622
`ethernet-products`	5,958
`server-products`	5,313

📝 Schema

The dataset is provided in parquet format. Each row represents a complete discussion thread.

Column	Type	Description
`discussion_id`	`string`	The unique ID of the forum topic/thread.
`subject`	`string`	The title or subject of the discussion.
`board`	`string`	The specific Intel hardware/software category.
`post_time`	`string`	ISO 8601 timestamp of the original question.
`has_solution`	`boolean`	`True` if an Intel engineer or community member provided an accepted solution.
`message_count`	`int64`	The total number of valid messages in the thread.
`messages_json`	`string`	A JSON-encoded string containing the full list of messages in chronological order.

🔍 Inside `messages_json`

[
  {
    "msg_id": "12345",
    "depth": 0,
    "author": "user_123",
    "post_time": "2023-01-01T12:00:00Z",
    "is_solution": false,
    "body": "I am getting Error 504 on my Xeon server...",
    "kudos": 2
  },
  {
    "msg_id": "12346",
    "depth": 1,
    "author": "Intel_Support",
    "post_time": "2023-01-01T12:15:00Z",
    "is_solution": true,
    "body": "Please update your BIOS to version X...",
    "kudos": 5
  }
]

🚀 Use Cases

Hardware Troubleshooting LLMs: Train models that understand complex server, networking, and processor error codes.
Reasoning (Chain-of-Thought): Train models on multi-turn troubleshooting processes, where users try solutions, fail, report back, and eventually succeed.
RAG (Retrieval-Augmented Generation): Build enterprise IT chatbots with factual troubleshooting grounding.

🔄 Updates & Automation

This dataset is maintained via an automated Delta Sync Pipeline. New discussions are fetched weekly via Discourse API cursors, sorted into discussion threads, and uploaded as updated/new chunk files without overwriting the existing historical archive. Full automation code is hosted in sphita/intel-community.

📜 Citation

If you use this dataset or crawler pipeline in your research, please cite:

@misc{intel_community_webcorpus_2026,
  title={Intel WebCorpus Forums: Enterprise Hardware Q&A Dataset},
  author={Verma, Adhyansh and Sphita AI Research},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/sphita/Intel-WebCorpus-forms}},
  note={Source Code: \url{https://github.com/sphita/intel-community}}
}

📄 License

This dataset is published under the GPL-3.0 License.