Update README.md

README.md

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 license: other
 license_name: open-data-attribution-training-disclosure-license-odatl-1.0
 license_link: LICENSE
+language:
+- in
+tags:
+- synthetic
 ---
+# token_efficiency_corpus
+
+A 2.5 GB CSV corpus teaching LLMs to minimise token usage in their outputs.
+Progresses from basic filler removal to expert-level nested reasoning compression.
+
+---
+
+## Contents
+
+verbose_output – The padded, wasteful version of the text
+efficient_output – The compressed, token-efficient equivalent
+technique – Compression strategy used
+subcategory - Specific variant of the technique
+difficulty – Tier 1 (easiest) -> Tier 8 (hardest)
+notes - Reserved for future metadata
+
+---
+
+## Difficulty tiers
+
+Tier 1 — remove_filler_words
+Strip padding phrases like "it is important to note that"
+"in order to", "due to the fact that", etc.
+
+Tier 2 — use_shorter_synonyms
+Replace long phrases with single-word equivalents.
+(utilise -> use, subsequently -> then, commence -> start ...).
+
+Tier 3 — remove_redundant_context
+Delete information the reader already knows from the
+surrounding text or conversation history.
+
+Tier 4 — use_structured_format
+Convert paragraph prose into key=value, lists, and
+machine-readable pipe-delimited strings.
+
+Tier 5 — omit_trivial_steps
+Remove deductions the reader can derive themselves;
+Jump straight to the conclusion.
+
+Tier 6 — deduplicate_cross_fields
+Reference shared attributes instead of repeating them.
+across multiple sentences or fields.
+
+Tier 7 — domain_shorthand
+Use field-standard abbreviations (MI, PCI, DAPT, TTL, TAR).
+that practitioners understand but novices do not need spelt out.
+
+Tier 8 — compress_nested_reasoning
+Collapse multi-step chains of logic (cause -> effect -> decision).
+into a single supported conclusion with the key constraints only.
+
+---
+
+## Training usage
+
+Load with pandas:
+
+import pandas as pd
+df = pd.read_csv("token_efficiency_corpus.csv")
+
+Supervised compression task:
+
+X = df["verbose_output"]
+y = df["efficient_output"]
+
+Classification task (identify compression technique):
+
+cls_y = df[["technique", "difficulty"]]
+
+Fine-tuning an LLM with the pair as input -> target:
+
+# input = "Compress this:\n{verbose_output}"
+# target = efficient_output
+
+---
+
+## Design notes
+
+- Streaming-first generation — rows are flushed to disc continuously,
+keeping peak memory small.
+- QUOTE_ALL CSV mode ensures commas and newlines inside fields
+Never break parsing.
+- Bidirectional training is possible with a reverse pass.
+(efficient -> verbose) to teach both compression and expansion.
+- Harder tiers get more rows because each example encodes a
+more nuanced compression insight.
+
+---
+
+## File layout
+
+.
+├── README. md                      - This file
+└── token_efficiency_corpus.csv – Corpus (~2.5 GB generated;
+target 5 GB with a larger
+AVG_ROW_BYTES_ESTIMATE)