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license: cc-by-nc-4.0
base_model: facebook/nllb-200-3.3B
language:
- en
- it
tags:
- translation
- subtitles
- fine-tuned
- experimental
- nllb
- italian
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.
## 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
```python
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](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):**
> ```
> @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}
> }
> ```
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