fix: support large input texts with chunking

[RSKKSOFFICIAL]

Fixes #71

What this does

Large input texts (>250 words) were being silently truncated by the model
because the output hit the decoding length limit mid-document. This adds a
chunking layer that splits the input into smaller pieces before translation,
then joins the results back into a single output.

Changes

lib/Service.py

• Added _chunk_text(): splits input at sentence boundaries into chunks of
  max 80 words. Hard-splits any single sentence that exceeds the limit.
• Added _join_chunks(): joins translated chunks in document order. Uses an
  empty string separator for no-space languages (zh, ja, th, etc.) and a
  single space for all others. Chunk order is always preserved regardless of
  source/target script direction — each chunk is already translated correctly
  by the model independently.
• Updated translate(): applies chunking when input exceeds the threshold,
  caps max_decoding_length proportionally per chunk to prevent runaway
  repetition loops, and enforces a minimum repetition_penalty per chunk.

config.json

• Added chunking section with four configurable parameters:
  • chunk_threshold (250): word count above which input is chunked
  • chunk_size (80): max words per chunk
  • min_repetition_penalty (1.5): lower bound for repetition penalty per chunk, prevents output loops on dense scripts like Devanagari
  • max_decoding_multiplier (3): output token cap as a multiple of input tokens per chunk

Testing

Tested with 350+ word inputs across 7 language pairs:

• English → German, French, Hindi, Arabic
• Arabic → English, Persian
• Persian → English

All pairs now produce complete output covering the full input. Before this
change every pair was truncated at roughly Section 03/04 of a 5-section test
document.

RTL languages (Arabic, Persian) are handled correctly, chunks are always
joined in forward document order since each chunk is translated independently.