|
--- |
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language: |
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- bs |
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- en |
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- hr |
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- sh |
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- sr |
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language_bcp47: |
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- bs_Latn |
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- sr_Cyrl |
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- sr_Latn |
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|
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tags: |
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- translation |
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- opus-mt-tc |
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|
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license: cc-by-4.0 |
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model-index: |
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- name: opus-mt-tc-base-en-sh |
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results: |
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- task: |
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name: Translation eng-hrv |
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type: translation |
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args: eng-hrv |
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dataset: |
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name: flores200-dev |
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type: flores200-dev |
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args: eng-hrv |
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metrics: |
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- name: BLEU |
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type: bleu |
|
value: 28.1 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.57963 |
|
- task: |
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name: Translation eng-srp_Cyrl |
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type: translation |
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args: eng-srp_Cyrl |
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dataset: |
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name: flores200-dev |
|
type: flores200-dev |
|
args: eng-srp_Cyrl |
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metrics: |
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- name: BLEU |
|
type: bleu |
|
value: 32.2 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.60096 |
|
- task: |
|
name: Translation eng-hrv |
|
type: translation |
|
args: eng-hrv |
|
dataset: |
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name: flores200-devtest |
|
type: flores200-devtest |
|
args: eng-hrv |
|
metrics: |
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- name: BLEU |
|
type: bleu |
|
value: 28.9 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.58652 |
|
- task: |
|
name: Translation eng-srp_Cyrl |
|
type: translation |
|
args: eng-srp_Cyrl |
|
dataset: |
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name: flores200-devtest |
|
type: flores200-devtest |
|
args: eng-srp_Cyrl |
|
metrics: |
|
- name: BLEU |
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type: bleu |
|
value: 31.7 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.59874 |
|
- task: |
|
name: Translation eng-hrv |
|
type: translation |
|
args: eng-hrv |
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dataset: |
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name: flores101-devtest |
|
type: flores_101 |
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args: eng hrv devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 28.7 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.586 |
|
- task: |
|
name: Translation eng-srp_Cyrl |
|
type: translation |
|
args: eng-srp_Cyrl |
|
dataset: |
|
name: flores101-devtest |
|
type: flores_101 |
|
args: eng srp_Cyrl devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 31.7 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.59874 |
|
- task: |
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name: Translation eng-bos_Latn |
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type: translation |
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args: eng-bos_Latn |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: eng-bos_Latn |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 46.3 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.666 |
|
- task: |
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name: Translation eng-hbs |
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type: translation |
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args: eng-hbs |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: eng-hbs |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 42.1 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.631 |
|
- task: |
|
name: Translation eng-hrv |
|
type: translation |
|
args: eng-hrv |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
|
args: eng-hrv |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 49.7 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.691 |
|
- task: |
|
name: Translation eng-srp_Cyrl |
|
type: translation |
|
args: eng-srp_Cyrl |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
|
args: eng-srp_Cyrl |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 45.1 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.645 |
|
- task: |
|
name: Translation eng-srp_Latn |
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type: translation |
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args: eng-srp_Latn |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
|
args: eng-srp_Latn |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 39.8 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.613 |
|
--- |
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# opus-mt-tc-base-en-sh |
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|
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## Table of Contents |
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- [Model Details](#model-details) |
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- [Uses](#uses) |
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- [Risks, Limitations and Biases](#risks-limitations-and-biases) |
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- [How to Get Started With the Model](#how-to-get-started-with-the-model) |
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- [Training](#training) |
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- [Evaluation](#evaluation) |
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- [Citation Information](#citation-information) |
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- [Acknowledgements](#acknowledgements) |
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|
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## Model Details |
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|
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Neural machine translation model for translating from English (en) to Serbo-Croatian (sh). |
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|
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This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). |
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**Model Description:** |
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- **Developed by:** Language Technology Research Group at the University of Helsinki |
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- **Model Type:** Translation (transformer-align) |
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- **Release**: 2021-04-20 |
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- **License:** CC-BY-4.0 |
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- **Language(s):** |
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- Source Language(s): eng |
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- Target Language(s): bos_Latn hbs hrv srp_Cyrl srp_Latn |
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- Language Pair(s): eng-bos_Latn eng-hbs eng-hrv eng-srp_Cyrl eng-srp_Latn |
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- Valid Target Language Labels: >>bos_Cyrl<< >>bos_Latn<< >>cnr<< >>cnr_Latn<< >>hbs<< >>hbs_Cyrl<< >>hrv<< >>srp_Cyrl<< >>srp_Latn<< |
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- **Original Model**: [opus+bt-2021-04-20.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hbs/opus+bt-2021-04-20.zip) |
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- **Resources for more information:** |
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- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) |
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- More information about released models for this language pair: [OPUS-MT eng-hbs README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hbs/README.md) |
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- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian) |
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- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/ |
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This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>bos_Latn<<` |
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|
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## Uses |
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|
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This model can be used for translation and text-to-text generation. |
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|
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## Risks, Limitations and Biases |
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**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.** |
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Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). |
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|
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## How to Get Started With the Model |
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|
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A short example code: |
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```python |
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from transformers import MarianMTModel, MarianTokenizer |
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src_text = [ |
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">>hrv<< You're about to make a very serious mistake.", |
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">>hbs<< I've just been too busy." |
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] |
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|
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model_name = "pytorch-models/opus-mt-tc-base-en-sh" |
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tokenizer = MarianTokenizer.from_pretrained(model_name) |
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model = MarianMTModel.from_pretrained(model_name) |
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translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) |
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|
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for t in translated: |
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print( tokenizer.decode(t, skip_special_tokens=True) ) |
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|
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# expected output: |
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# Ti si o tome napraviti vrlo ozbiljnu pogrešku. |
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# [4] |
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``` |
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You can also use OPUS-MT models with the transformers pipelines, for example: |
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```python |
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from transformers import pipeline |
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pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-en-sh") |
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print(pipe(">>hrv<< You're about to make a very serious mistake.")) |
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# expected output: Ti si o tome napraviti vrlo ozbiljnu pogrešku. |
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``` |
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|
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## Training |
|
|
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- **Data**: opus+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) |
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- **Pre-processing**: SentencePiece (spm32k,spm32k) |
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- **Model Type:** transformer-align |
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- **Original MarianNMT Model**: [opus+bt-2021-04-20.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hbs/opus+bt-2021-04-20.zip) |
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- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) |
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|
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## Evaluation |
|
|
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* test set translations: [opus+bt-2021-04-20.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hbs/opus+bt-2021-04-20.test.txt) |
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* test set scores: [opus+bt-2021-04-20.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hbs/opus+bt-2021-04-20.eval.txt) |
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* benchmark results: [benchmark_results.txt](benchmark_results.txt) |
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* benchmark output: [benchmark_translations.zip](benchmark_translations.zip) |
|
|
|
| langpair | testset | chr-F | BLEU | #sent | #words | |
|
|----------|---------|-------|-------|-------|--------| |
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| eng-bos_Latn | tatoeba-test-v2021-08-07 | 0.666 | 46.3 | 301 | 1650 | |
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| eng-hbs | tatoeba-test-v2021-08-07 | 0.631 | 42.1 | 10017 | 63927 | |
|
| eng-hrv | tatoeba-test-v2021-08-07 | 0.691 | 49.7 | 1480 | 9396 | |
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| eng-srp_Cyrl | tatoeba-test-v2021-08-07 | 0.645 | 45.1 | 1580 | 9152 | |
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| eng-srp_Latn | tatoeba-test-v2021-08-07 | 0.613 | 39.8 | 6656 | 43729 | |
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| eng-hrv | flores101-devtest | 0.586 | 28.7 | 1012 | 22423 | |
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| eng-hrv | flores200-dev | 0.57963 | 28.1 | 997 | 21567 | |
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| eng-hrv | flores200-devtest | 0.58652 | 28.9 | 1012 | 22423 | |
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| eng-srp_Cyrl | flores101-devtest | 0.59874 | 31.7 | 1012 | 23456 | |
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| eng-srp_Cyrl | flores200-dev | 0.60096 | 32.2 | 997 | 22384 | |
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| eng-srp_Cyrl | flores200-devtest | 0.59874 | 31.7 | 1012 | 23456 | |
|
|
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## Citation Information |
|
|
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* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) |
|
|
|
``` |
|
@inproceedings{tiedemann-thottingal-2020-opus, |
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title = "{OPUS}-{MT} {--} Building open translation services for the World", |
|
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, |
|
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", |
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month = nov, |
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year = "2020", |
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address = "Lisboa, Portugal", |
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publisher = "European Association for Machine Translation", |
|
url = "https://aclanthology.org/2020.eamt-1.61", |
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pages = "479--480", |
|
} |
|
|
|
@inproceedings{tiedemann-2020-tatoeba, |
|
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", |
|
author = {Tiedemann, J{\"o}rg}, |
|
booktitle = "Proceedings of the Fifth Conference on Machine Translation", |
|
month = nov, |
|
year = "2020", |
|
address = "Online", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://aclanthology.org/2020.wmt-1.139", |
|
pages = "1174--1182", |
|
} |
|
``` |
|
|
|
## Acknowledgements |
|
|
|
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. |
|
|
|
## Model conversion info |
|
|
|
* transformers version: 4.16.2 |
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* OPUS-MT git hash: e2a6299 |
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* port time: Tue Oct 11 10:14:32 CEST 2022 |
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* port machine: LM0-400-22516.local |
|
|
