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--- |
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language: |
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- en |
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- lv |
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tags: |
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- translation |
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license: cc-by-4.0 |
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datasets: |
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- quickmt/quickmt-train.lv-en |
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model-index: |
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- name: quickmt-lv-en |
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results: |
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- task: |
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name: Translation lav-eng |
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type: translation |
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args: lav-eng |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: lvs_Latn eng_Latn devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 35.3 |
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- name: CHRF |
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type: chrf |
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value: 62.91 |
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- name: COMET |
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type: comet |
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value: 86.98 |
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--- |
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# `quickmt-lv-en` Neural Machine Translation Model |
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`quickmt-lv-en` is a reasonably fast and reasonably accurate neural machine translation model for translation from `lv` into `en`. |
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## Try it on our Huggingface Space |
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Give it a try before downloading here: https://huggingface.co/spaces/quickmt/QuickMT-Demo |
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## Model Information |
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* Trained using [`eole`](https://github.com/eole-nlp/eole) |
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* 200M parameter transformer 'big' with 8 encoder layers and 2 decoder layers |
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* 32k separate Sentencepiece vocabs |
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* Expested for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format |
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* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.lv-en/tree/main |
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See the `eole` model configuration in this repository for further details and the `eole-model` for the raw `eole` (pytorch) model. |
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## Usage with `quickmt` |
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You must install the Nvidia cuda toolkit first, if you want to do GPU inference. |
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Next, install the `quickmt` python library and download the model: |
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```bash |
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git clone https://github.com/quickmt/quickmt.git |
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pip install ./quickmt/ |
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quickmt-model-download quickmt/quickmt-lv-en ./quickmt-lv-en |
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``` |
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Finally use the model in python: |
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```python |
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from quickmt import Translator |
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# Auto-detects GPU, set to "cpu" to force CPU inference |
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t = Translator("./quickmt-lv-en/", device="auto") |
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# Translate - set beam size to 1 for faster speed (but lower quality) |
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sample_text = 'Dr Ehud Ur, będący profesorem medycyny na Uniwersytecie Dalhousie w Halifaxie w Nowej Szkocji oraz przewodniczącym oddziału klinicznego i naukowego Kanadyjskiego Stowarzyszenia Cukrzycy, przestrzegł, iż badania nadal dopiero się zaczynają.' |
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t(sample_text, beam_size=5) |
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``` |
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> 'Dr. Ehud Ur, a professor of medicine at Dalhousie University in Halifax, Nova Scotia and chairman of the clinical and scientific division of the Canadian Diabetes Association, warned that research is still just beginning.' |
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```python |
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# Get alternative translations by sampling |
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# You can pass any cTranslate2 `translate_batch` arguments |
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t([sample_text], sampling_temperature=1.2, beam_size=1, sampling_topk=50, sampling_topp=0.9) |
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``` |
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> 'Professor of Medicine at Dalhous University Halifax in Nova Scotia, MD and Chair of the Canadian Diabetes Association’s Clinical and Scientific Division, cautioned that research is just beginning.' |
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The model is in `ctranslate2` format, and the tokenizers are `sentencepiece`, so you can use `ctranslate2` directly instead of through `quickmt`. It is also possible to get this model to work with e.g. [LibreTranslate](https://libretranslate.com/) which also uses `ctranslate2` and `sentencepiece`. A model in safetensors format to be used with `eole` is also provided. |
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## Metrics |
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`bleu` and `chrf2` are calculated with [sacrebleu](https://github.com/mjpost/sacrebleu) on the [Flores200 `devtest` test set](https://huggingface.co/datasets/facebook/flores) ("pol_Latn"->"eng_Latn"). `comet22` with the [`comet`](https://github.com/Unbabel/COMET) library and the [default model](https://huggingface.co/Unbabel/wmt22-comet-da). "Time (s)" is the time in seconds to translate the flores-devtest dataset (1012 sentences) on an RTX 4070s GPU with batch size 32 (faster speed is possible using a larger batch size). |
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| | bleu | chrf2 | comet22 | Time (s) | |
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|:---------------------------------|-------:|--------:|----------:|-----------:| |
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| quickmt/quickmt-lv-en | 35.3 | 62.91 | 86.98 | 1.39 | |
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| Helsinki-NLP/opus-mt-lv-en | 30.86 | 59.56 | 85.23 | 3.61 | |
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| facebook/nllb-200-distilled-600M | 32.14 | 59.13 | 84.62 | 21.92 | |
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| facebook/nllb-200-distilled-1.3B | 36.63 | 62.59 | 86.86 | 37.95 | |
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| facebook/m2m100_418M | 27.01 | 56.61 | 81.66 | 18.57 | |
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| facebook/m2m100_1.2B | 33.61 | 61.31 | 86.15 | 35.87 | |
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