Chinasky71/nllb-200-3.3b-it-subtitles

NLLB-200 3.3B — Fine-Tuned EN→IT Subtitles (v3 · Experimental)

⚠️ Experimental / Test Release — This model has been fine-tuned for research and personal use. It has not been evaluated on standard benchmarks and may produce errors, especially outside the subtitle domain. Use at your own risk.

Model Description

This is a fine-tuned version of facebook/nllb-200-3.3B, optimised for English → Italian translation of cinematic and TV subtitles, with emphasis on colloquial language, slang, and natural dialogue.

The fine-tuning was performed in three successive rounds (continual learning) without modifying the model architecture.

Training Details

Stage	Dataset	Pairs	Best eval_loss
v1	Subtitle pairs EN-IT	576 k	1.1629 (step 35 500)
v2	Continual FT — subtitle + TV corpus	1.99 M	1.2296
v3 (Stage 2)	Slang/colloquial — GPT-4 synthetic (21 k) + TV anchors (9 k)	29.4 k	1.4008

Optimised for:

• Cinematic and TV series subtitles
• Colloquial language, slang expressions, natural dialogue
• Register preservation (formal, informal, humorous)

Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Chinasky71/nllb-200-3.3b-it-subtitles")
model = AutoModelForSeq2SeqLM.from_pretrained("Chinasky71/nllb-200-3.3b-it-subtitles")

inputs = tokenizer("Hello, how are you?", return_tensors="pt",
                   src_lang="eng_Latn")
output = model.generate(**inputs,
                        forced_bos_token_id=tokenizer.convert_tokens_to_ids("ita_Latn"))
print(tokenizer.decode(output[0], skip_special_tokens=True))

Limitations

• Evaluated only on subtitle-domain text; general-purpose translation quality may be lower than the base model
• v3 slang fine-tuning slightly increased eval_loss on the held-out set — the model trades general accuracy for colloquial fluency
• Not suitable for medical, legal, or safety-critical applications

Credits & License

This model is derived from facebook/nllb-200-3.3B developed by Meta AI as part of the No Language Left Behind project.

All credit for the base architecture, pre-training data, and original weights belongs to Meta AI. This fine-tuned derivative is released under the same CC BY-NC 4.0 license as the original model.

Citation (original model):

@article{nllb2022,
  title={No Language Left Behind: Scaling Human-Centered Machine Translation},
  author={{NLLB Team} and Costa-juss\`a, Marta R. and others},
  journal={arXiv preprint arXiv:2207.04672},
  year={2022}
}