edgar-bpe-65536

edgar-bpe-65536

A byte-level BPE tokenizer trained exclusively on SEC filings, drawn from a 4,356-document, 1.3 GB cleaned EDGAR corpus (2010-2024, ~4,250 companies; exact training-set size below). The fair-fight variant: identical pre-tokenization rules to GPT-2/GPT-4/GPT-4o/Qwen3, so the comparison isolates vocabulary.

Every number below is measured on 216 held-out documents no variant ever trained on; a 450-document cross-form probe (10-Q, 8-K, DEF 14A) confirmed the margins generalize past the training form. Full methodology, benchmark tables, and the corpus pipeline: write-up · repository.

Usage

from transformers import AutoTokenizer
tok = AutoTokenizer.from_pretrained("tcontorno/edgar-bpe-65536")
tok("Revenues of $3,232,193 for fiscal 2023, an increase of 4.25%.")

or with the tokenizers library directly:

from tokenizers import Tokenizer
tok = Tokenizer.from_file("tokenizer.json")

Training

Vocab size 65,536, min merge frequency 2, pre-tokenization mode gpt2, 4,140 training documents, one <|endoftext|> special token. Training corpus built from primary filing documents (iXBRL/HTML cleaned to text with numbers and real Unicode preserved); 5% held-out split with content-hash deduplication before any training.

Caveats

Trained with standard GPT-2-style byte-level pre-tokenization; no special caveats. A tokenizer is bound to the model trained with it, so this is a drop-in for new models and tokenizer research, not for existing checkpoints. Compression is the measured claim; see the write-up for the full limitations section.

Part of the edgar-tok project

• Tokenizers: edgar-bpe-65536 · edgar-bpe-65536-digits · edgar-bpe-131072-line
• Dataset: EDGAR M&A Deal Events (2010–2024)
• Code & benchmark: github.com/tcontorno6/edgar-tok
• Write-up: Half the tokens: training a BPE tokenizer on SEC filings