| language: | |
| - en | |
| - cy | |
| pipeline_tag: translation | |
| tags: | |
| - translation | |
| - marian | |
| metrics: | |
| - bleu | |
| - cer | |
| - wer | |
| - wil | |
| - wip | |
| - chrf | |
| license: apache-2.0 | |
| model-index: | |
| - name: "mt-dspec-health-en-cy" | |
| results: | |
| - task: | |
| name: Translation | |
| type: translation | |
| metrics: | |
| - name: SacreBLEU | |
| type: bleu | |
| value: 54.16 | |
| - name: CER | |
| type: cer | |
| value: 0.31 | |
| - name: WER | |
| type: wer | |
| value: 0.47 | |
| - name: WIL | |
| type: wil | |
| value: 0.67 | |
| - name: WIP | |
| type: wip | |
| value: 0.33 | |
| - name: SacreBLEU CHRF | |
| type: chrf | |
| value: 69.03 | |
| --- | |
| # mt-dspec-health-en-cy | |
| A language translation model for translating between English and Welsh, specialised to the specific domain of Health and care. | |
| This model was trained using custom DVC pipeline employing [Marian NMT](https://marian-nmt.github.io/), | |
| the datasets prepared were generated from the following sources: | |
| - [UK Government Legislation data](https://www.legislation.gov.uk) | |
| - [OPUS-cy-en](https://opus.nlpl.eu/) | |
| - [Cofnod Y Cynulliad](https://record.assembly.wales/) | |
| - [Cofion Techiaith Cymru](https://cofion.techiaith.cymru) | |
| The data was split into train, validation and tests sets, the test set containing health-specific segments from TMX files | |
| selected at random from the [Cofion Techiaith Cymru](https://cofion.techiaith.cymru) website, which have been pre-classified as pertaining to the specific domain. | |
| Having extracted the test set, the aggregation of remaining data was then split into 10 training and validation sets, and fed into 10 marian training sessions. | |
| A website demonstrating use of this model is available at http://cyfieithu.techiaith.cymru. | |
| ## Evaluation | |
| Evaluation was done using the python libraries [SacreBLEU](https://github.com/mjpost/sacrebleu) and [torchmetrics](https://torchmetrics.readthedocs.io/en/stable/). | |
| ## Usage | |
| Ensure you have the prerequisite python libraries installed: | |
| ```bash | |
| pip install transformers sentencepiece | |
| ``` | |
| ```python | |
| import trnasformers | |
| model_id = "techiaith/mt-spec-health-en-cy" | |
| tokenizer = transformers.AutoTokenizer.from_pretrained(model_id) | |
| model = transformers.AutoModelForSeq2SeqLM.from_pretrained(model_id) | |
| translate = transformers.pipeline("translation", model=model, tokenizer=tokenizer) | |
| translated = translate("The doctor will be late to attend to patients this morning.") | |
| print(translated["translation_text"]) | |
| ``` | |