| ---
|
| license: cc-by-nc-4.0
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| base_model: facebook/nllb-200-3.3B
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| language:
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| - en
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| - it
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| tags:
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| - translation
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| - subtitles
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| - fine-tuned
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| - experimental
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| - nllb
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| - italian
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| pipeline_tag: translation
|
| ---
|
|
|
| # 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](https://huggingface.co/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.
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|
|
| ## Training Details
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|
|
| | Stage | Dataset | Pairs | Best eval_loss |
|
| |---|---|---|---|
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| | **v1** | Subtitle pairs EN-IT | 576 k | 1.1629 (step 35 500) |
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| | **v2** | Continual FT — subtitle + TV corpus | 1.99 M | 1.2296 |
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| | **v3** (Stage 2) | Slang/colloquial — GPT-4 synthetic (21 k) + TV anchors (9 k) | 29.4 k | 1.4008 |
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|
|
| **Optimised for:**
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| - Cinematic and TV series subtitles
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| - Colloquial language, slang expressions, natural dialogue
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| - Register preservation (formal, informal, humorous)
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|
|
| ## Usage
|
|
|
| ```python
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| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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|
|
| tokenizer = AutoTokenizer.from_pretrained("Chinasky71/nllb-200-3.3b-it-subtitles")
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| model = AutoModelForSeq2SeqLM.from_pretrained("Chinasky71/nllb-200-3.3b-it-subtitles")
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|
|
| inputs = tokenizer("Hello, how are you?", return_tensors="pt",
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| src_lang="eng_Latn")
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| output = model.generate(**inputs,
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| forced_bos_token_id=tokenizer.convert_tokens_to_ids("ita_Latn"))
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| 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
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| - v3 slang fine-tuning slightly increased eval_loss on the held-out set — the model trades general accuracy for colloquial fluency
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| - Not suitable for medical, legal, or safety-critical applications
|
|
|
| ## Credits & License
|
|
|
| This model is derived from **[facebook/nllb-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B)** developed by **Meta AI** as part of the [No Language Left Behind](https://ai.facebook.com/research/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](https://creativecommons.org/licenses/by-nc/4.0/)** license as the original model.
|
|
|
| > **Citation (original model):**
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| > ```
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| > @article{nllb2022,
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| > title={No Language Left Behind: Scaling Human-Centered Machine Translation},
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| > author={{NLLB Team} and Costa-juss\`a, Marta R. and others},
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| > journal={arXiv preprint arXiv:2207.04672},
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| > year={2022}
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| > }
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| > ```
|
| |
