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--- |
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language: |
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- en |
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- de |
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- it |
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- el |
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- es |
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- nl |
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pipeline_tag: translation |
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license: apache-2.0 |
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--- |
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The model and the tokenizer are based on [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M). |
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We trained the model to use one sentence of context and a single term of the terminology-constraint. The context is prepended to the input sentence with the `sep_token` in between. The term should be in the target language and be postpended to the input sentence with the `sep_token` in between. In case of no terminology constraint, the `sep_token` should also be added. 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. |
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The tokenizer of the base model was not changed. For the language codes, see the base model. |
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Use this code for translation: |
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``` |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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model_name = 'voxreality/src_ctx_and_term_nllb_600M' |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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max_length = 100 |
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src_lang = 'eng_Latn' |
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tgt_lang = 'deu_Latn' |
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context_text = 'This is an optional context sentence.' |
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target_term = 'text' # term to be used in the target language |
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sentence_text = 'Text to be translated.' |
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# if a context and a term are provided use the following: |
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input_text = f'{context_text} {tokenizer.sep_token} {sentence_text} {tokenizer.sep_token} {target_term}' |
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# if no context but a term is provided use the following: |
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# input_text = f'{sentence_text} {tokenizer.sep_token} {target_term}' |
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# if a context is provided but no term use the following: |
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# input_text = f'{context_text} {tokenizer.sep_token} {sentence_text} {tokenizer.sep_token}' |
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# if not context nor term is provided use the following: |
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# input_text = f'{sentence_text} {tokenizer.sep_token}' |
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tokenizer.src_lang = src_lang |
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inputs = tokenizer(input_text, return_tensors='pt').to(model.device) |
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model_output = model.generate(**inputs, |
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forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang], |
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max_length=max_length) |
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output_text = tokenizer.batch_decode(model_output, skip_special_tokens=True)[0] |
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print(output_text) |
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``` |
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You can also use the pipeline |
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``` |
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from transformers import pipeline |
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model_name = 'voxreality/src_ctx_and_term_nllb_600M' |
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translation_pipeline = pipeline("translation", model=model_name) |
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sep_token = translation_pipeline.tokenizer.sep_token |
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src_lang = 'eng_Latn' |
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tgt_lang = 'deu_Latn' |
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context_text = 'This is an optional context sentence.' |
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target_term = 'text' # term to be used in the target language |
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sentence_text = 'Text to be translated.' |
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# if a context and a term are provided use the following: |
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input_texts = [f'{context_text} {sep_token} {sentence_text} {sep_token} {target_term}'] |
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# if no context but a term is provided use the following: |
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# input_texts = [f'{sentence_text} {sep_token} {target_term}'] |
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# if a context is provided but no term use the following: |
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# input_texts = [f'{context_text} {sep_token} {sentence_text} {sep_token}'] |
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# if not context nor term is provided use the following: |
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# input_texts = [f'{sentence_text} {sep_token}'] |
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pipeline_output = translation_pipeline(input_texts, src_lang=src_lang, tgt_lang=tgt_lang) |
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print(pipeline_output[0]['translation_text']) |
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``` |
