how do we prepare our messy internal pdf files before trying to fine tune an open source language model on them
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
how do we prepare our messy internal pdf files before trying to fine tune an open source language model on them
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 1 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
best practices prepare PDF documents for LLM fine tuning OCR document parsing deduplication instruction dataset JSONL Hugging Face Google ↗
Docling documentation PDF parsing OCR tables document structure export JSON Google ↗
- Documentation · docling.org
- GitHub · github.com
- TRL documentation · huggingface.co
- Website · python.org
- Preparing data for fine-tuning LLMs · developer.ibm.com
- Docling — Turn complex documents into structured data your AI can use · docling.ai
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
best practices prepare PDF documents for LLM fine tuning OCR document parsing deduplication instruction dataset JSONL Hugging Face OBSERVED | Google ↗ AI Mode ↗ | — | Instruction | — | — | 1x of 1 | |
Docling documentation PDF parsing OCR tables document structure export JSON OBSERVED | Google ↗ AI Mode ↗ | — | Instruction | — | — | 1x of 1 |