BHARATI: Morphology-Aware Tokenizers for Classical Indian Languages with Subword Fertility Analysis

Researchers have developed BHARATI, a set of SentencePiece BPE tokenizers for classical Indian languages, achieving a 90% reduction in sequence length compared to GPT-2 on a held-out test set of 490 IKS-domain sentences. The v3 tokenizer averages 2.6 tokens per IKS technical term versus 5.25 for GPT-2, directly increasing effective context length for downstream language models.

arXiv:2607.23319v1 Announce Type: new Abstract: Standard subword tokenization algorithms such as Byte-Pair Encoding BPE and SentencePiece are trained predominantly on modern language corpora and produce inefficient segmentations when applied to classical Indian languages. Sanskrit, Tamil, and other classical Indic languages exhibit agglutinative morphology, productive sandhi phonological fusion at word boundaries , and domain-specific vocabularies absent from general-purpose training data. This paper presents BHARATI, a set of SentencePiece BPE tokenizers trained on a balanced 781 MB corpus spanning seven languages English, Hindi, Sanskrit, Tamil, Telugu, Kannada, and Malayalam with native script support for all languages. We describe three successive tokenizer versions: v1 English and Sanskrit only, with broken byte-fallback for Tamil , v2 four-language support with byte-level fallback for southern languages , and v3 full seven-language native subword coverage . Subword fertility analysis demonstrates that v3 averages 2.6 tokens per Indian Knowledge System IKS technical term, compared to 5.25 tokens per term with GPT-2's tokenizer and 3.75 tokens with the multilingual SentencePiece baseline, with the largest gains on a set of reserved IKS terms that are represented as single tokens by construction. On a held-out test set of 490 IKS-domain sentences 70 per language across seven languages, released with the measurement script , v3 reduces sequence length by roughly 90% relative to GPT-2 and byte-level encoding which lack native Indic subwords and by approximately 25% relative to the mBART-50 multilingual baseline, averaged across the six Indic languages, directly translating to increased effective context length for downstream language models. The tokenizer models 32,000 vocabulary , training scripts, and evaluation benchmarks are released under open licenses.