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---
language:
- en
- de
- nl
- it
- el
- es
pipeline_tag: translation
license: apache-2.0
---
The model and the tokenizer are based on [facebook/nllb-200-1.3B]( https://huggingface.co/facebook/nllb-200-1.3B).
We trained the model to use one sentence of context. The context is prepended to the input sentence with the `sep_token` in between. We used a subset of the [OpenSubtitles2018]( https://huggingface.co/datasets/open_subtitles) dataset for training. We trained on the interleaved dataset for all directions between the following languages: English, German, Dutch, Spanish, Italian, and Greek.
The tokenizer of the base model was not changed. For the language codes, see the base model.
Use this code for translation:
``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_name = 'voxreality/src_ctx_aware_nllb_1.3B'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
max_length = 100
src_lang = 'eng_Latn'
tgt_lang = 'deu_Latn'
context_text = 'This is an optional context sentence.'
sentence_text = 'Text to be translated.'
# if the context is provided use the following:
input_text = f'{context_text} {tokenizer.sep_token} {sentence_text}'
# if no context is provided use the following:
# input_text = sentence_text
tokenizer.src_lang = src_lang
inputs = tokenizer(input_text, return_tensors='pt').to(model.device)
model_output = model.generate(**inputs,
forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang],
max_length=max_length)
output_text = tokenizer.batch_decode(model_output, skip_special_tokens=True)[0]
print(output_text)
```
You can also use the pipeline
```
from transformers import pipeline
model_name = 'voxreality/src_ctx_aware_nllb_1.3B'
translation_pipeline = pipeline("translation", model=model_name)
src_lang = 'eng_Latn'
tgt_lang = 'deu_Latn'
context_text = 'This is an optional context sentence.'
sentence_text = 'Text to be translated.'
# if the context is provided use the following:
input_texts = [f'{context_text} {tokenizer.sep_token} {sentence_text}']
# if no context is provided use the following:
# input_texts = [sentence_text]
pipeline_output = translation_pipeline(input_texts, src_lang=src_lang, tgt_lang=tgt_lang)
print(pipeline_output[0]['translation_text'])
``` |