Initial commit
Browse files- .gitattributes +1 -0
- README.md +286 -0
- benchmark_results.txt +16 -0
- benchmark_translations.zip +3 -0
- config.json +45 -0
- pytorch_model.bin +3 -0
- source.spm +3 -0
- special_tokens_map.json +1 -0
- target.spm +3 -0
- tokenizer_config.json +1 -0
- vocab.json +0 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.spm filter=lfs diff=lfs merge=lfs -text
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README.md
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@@ -0,0 +1,286 @@
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1 |
+
---
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2 |
+
language:
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+
- da
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+
- de
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+
- gmq
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- is
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- nb
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- nn
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- no
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- sv
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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-big-de-gmq
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+
results:
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+
- task:
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+
name: Translation deu-dan
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+
type: translation
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+
args: deu-dan
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+
dataset:
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+
name: flores101-devtest
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+
type: flores_101
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+
args: deu dan devtest
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+
metrics:
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+
- name: BLEU
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+
type: bleu
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+
value: 35.6
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+
- name: chr-F
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+
type: chrf
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+
value: 0.62363
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+
- task:
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name: Translation deu-isl
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type: translation
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args: deu-isl
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+
dataset:
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+
name: flores101-devtest
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type: flores_101
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+
args: deu isl devtest
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+
metrics:
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+
- name: BLEU
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+
type: bleu
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+
value: 20.6
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+
- name: chr-F
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+
type: chrf
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+
value: 0.48691
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+
- task:
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name: Translation deu-nob
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type: translation
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+
args: deu-nob
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+
dataset:
|
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+
name: flores101-devtest
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type: flores_101
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args: deu nob devtest
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+
metrics:
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- name: BLEU
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+
type: bleu
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+
value: 25.3
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+
- name: chr-F
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+
type: chrf
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value: 0.55765
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+
- task:
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name: Translation deu-swe
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type: translation
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args: deu-swe
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dataset:
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name: flores101-devtest
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type: flores_101
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args: deu swe devtest
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metrics:
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- name: BLEU
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+
type: bleu
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value: 34.7
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- name: chr-F
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type: chrf
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value: 0.62323
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+
- task:
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name: Translation deu-dan
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type: translation
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args: deu-dan
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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: deu-dan
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metrics:
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- name: BLEU
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type: bleu
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+
value: 58.7
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- name: chr-F
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type: chrf
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value: 0.74306
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+
- task:
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name: Translation deu-isl
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type: translation
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args: deu-isl
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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: deu-isl
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metrics:
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+
- name: BLEU
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type: bleu
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value: 47.1
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- name: chr-F
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type: chrf
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value: 0.65180
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+
- task:
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name: Translation deu-nob
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type: translation
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args: deu-nob
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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: deu-nob
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metrics:
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- name: BLEU
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+
type: bleu
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+
value: 52.5
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- name: chr-F
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type: chrf
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value: 0.71062
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+
- task:
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name: Translation deu-swe
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type: translation
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args: deu-swe
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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: deu-swe
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metrics:
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- name: BLEU
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type: bleu
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value: 58.3
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- name: chr-F
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type: chrf
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value: 0.72658
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---
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+
# opus-mt-tc-big-de-gmq
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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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## Model Details
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Neural machine translation model for translating from German (de) to North Germanic languages (gmq).
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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-big)
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- **Release**: 2022-07-25
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- **License:** CC-BY-4.0
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- **Language(s):**
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- Source Language(s): deu
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- Target Language(s): dan isl nno nob nor swe
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- Valid Target Language Labels: >>dan<< >>isl<< >>nno<< >>nob<< >>nor<< >>swe<<
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- **Original Model**: [opusTCv20210807_transformer-big_2022-07-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-gmq/opusTCv20210807_transformer-big_2022-07-25.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 deu-gmq README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-gmq/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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+
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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. `>>dan<<`
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## Uses
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This model can be used for translation and text-to-text generation.
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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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## How to Get Started With the Model
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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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">>dan<< Ich hätte fast meinen Pass vergessen.",
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">>dan<< Dieses Fenster hier ist schusssicher."
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]
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model_name = "pytorch-models/opus-mt-tc-big-de-gmq"
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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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for t in translated:
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print( tokenizer.decode(t, skip_special_tokens=True) )
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# expected output:
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# Jeg havde næsten glemt mit pas.
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# Dette vindue er skudsikkert.
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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-big-de-gmq")
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print(pipe(">>dan<< Ich hätte fast meinen Pass vergessen."))
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# expected output: Jeg havde næsten glemt mit pas.
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```
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## Training
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- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
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- **Pre-processing**: SentencePiece (spm32k,spm32k)
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- **Model Type:** transformer-big
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- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-gmq/opusTCv20210807_transformer-big_2022-07-25.zip)
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- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
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## Evaluation
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* test set translations: [opusTCv20210807_transformer-big_2022-07-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-gmq/opusTCv20210807_transformer-big_2022-07-25.test.txt)
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* test set scores: [opusTCv20210807_transformer-big_2022-07-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-gmq/opusTCv20210807_transformer-big_2022-07-25.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)
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| langpair | testset | chr-F | BLEU | #sent | #words |
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|----------|---------|-------|-------|-------|--------|
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| deu-dan | tatoeba-test-v2021-08-07 | 0.74306 | 58.7 | 9998 | 74644 |
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| deu-isl | tatoeba-test-v2021-08-07 | 0.65180 | 47.1 | 969 | 5951 |
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| deu-nob | tatoeba-test-v2021-08-07 | 0.71062 | 52.5 | 3525 | 31978 |
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+
| deu-swe | tatoeba-test-v2021-08-07 | 0.72658 | 58.3 | 3410 | 22701 |
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| deu-dan | flores101-devtest | 0.62363 | 35.6 | 1012 | 24638 |
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| deu-isl | flores101-devtest | 0.48691 | 20.6 | 1012 | 22834 |
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| deu-nob | flores101-devtest | 0.55765 | 25.3 | 1012 | 23873 |
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| deu-swe | flores101-devtest | 0.62323 | 34.7 | 1012 | 23121 |
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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.)
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```
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@inproceedings{tiedemann-thottingal-2020-opus,
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title = "{OPUS}-{MT} {--} Building open translation services for the World",
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author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
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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",
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url = "https://aclanthology.org/2020.eamt-1.61",
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pages = "479--480",
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}
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@inproceedings{tiedemann-2020-tatoeba,
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title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
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author = {Tiedemann, J{\"o}rg},
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booktitle = "Proceedings of the Fifth Conference on Machine Translation",
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month = nov,
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year = "2020",
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address = "Online",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2020.wmt-1.139",
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pages = "1174--1182",
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}
|
275 |
+
```
|
276 |
+
|
277 |
+
## Acknowledgements
|
278 |
+
|
279 |
+
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.
|
280 |
+
|
281 |
+
## Model conversion info
|
282 |
+
|
283 |
+
* transformers version: 4.16.2
|
284 |
+
* OPUS-MT git hash: 8b9f0b0
|
285 |
+
* port time: Fri Aug 12 18:54:58 EEST 2022
|
286 |
+
* port machine: LM0-400-22516.local
|
benchmark_results.txt
ADDED
@@ -0,0 +1,16 @@
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
1 |
+
deu-dan flores101-dev 0.61917 36.1 997 23685
|
2 |
+
deu-isl flores101-dev 0.48506 20.1 997 21857
|
3 |
+
deu-nob flores101-dev 0.55184 25.5 997 23157
|
4 |
+
deu-swe flores101-dev 0.62102 34.8 997 22417
|
5 |
+
deu-dan flores101-devtest 0.62363 35.6 1012 24638
|
6 |
+
deu-isl flores101-devtest 0.48691 20.6 1012 22834
|
7 |
+
deu-nob flores101-devtest 0.55765 25.3 1012 23873
|
8 |
+
deu-swe flores101-devtest 0.62323 34.7 1012 23121
|
9 |
+
deu-dan tatoeba-test-v2020-07-28 0.74305 58.7 10000 74655
|
10 |
+
deu-isl tatoeba-test-v2020-07-28 0.65276 47.2 968 5939
|
11 |
+
deu-dan tatoeba-test-v2021-03-30 0.74305 58.7 10000 74655
|
12 |
+
deu-isl tatoeba-test-v2021-03-30 0.65393 47.3 990 6127
|
13 |
+
deu-dan tatoeba-test-v2021-08-07 0.74306 58.7 9998 74644
|
14 |
+
deu-isl tatoeba-test-v2021-08-07 0.65180 47.1 969 5951
|
15 |
+
deu-nob tatoeba-test-v2021-08-07 0.71062 52.5 3525 31978
|
16 |
+
deu-swe tatoeba-test-v2021-08-07 0.72658 58.3 3410 22701
|
benchmark_translations.zip
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
|
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:8cd1261fb584ea7713d5dea17ddaf3fb5be08dd0f7762e278871d73da923c81e
|
3 |
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size 2720078
|
config.json
ADDED
@@ -0,0 +1,45 @@
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|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"activation_dropout": 0.0,
|
3 |
+
"activation_function": "relu",
|
4 |
+
"architectures": [
|
5 |
+
"MarianMTModel"
|
6 |
+
],
|
7 |
+
"attention_dropout": 0.0,
|
8 |
+
"bad_words_ids": [
|
9 |
+
[
|
10 |
+
56727
|
11 |
+
]
|
12 |
+
],
|
13 |
+
"bos_token_id": 0,
|
14 |
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"classifier_dropout": 0.0,
|
15 |
+
"d_model": 1024,
|
16 |
+
"decoder_attention_heads": 16,
|
17 |
+
"decoder_ffn_dim": 4096,
|
18 |
+
"decoder_layerdrop": 0.0,
|
19 |
+
"decoder_layers": 6,
|
20 |
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"decoder_start_token_id": 56727,
|
21 |
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"decoder_vocab_size": 56728,
|
22 |
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"dropout": 0.1,
|
23 |
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"encoder_attention_heads": 16,
|
24 |
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"encoder_ffn_dim": 4096,
|
25 |
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"encoder_layerdrop": 0.0,
|
26 |
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"encoder_layers": 6,
|
27 |
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"eos_token_id": 41373,
|
28 |
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"forced_eos_token_id": 41373,
|
29 |
+
"init_std": 0.02,
|
30 |
+
"is_encoder_decoder": true,
|
31 |
+
"max_length": 512,
|
32 |
+
"max_position_embeddings": 1024,
|
33 |
+
"model_type": "marian",
|
34 |
+
"normalize_embedding": false,
|
35 |
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"num_beams": 4,
|
36 |
+
"num_hidden_layers": 6,
|
37 |
+
"pad_token_id": 56727,
|
38 |
+
"scale_embedding": true,
|
39 |
+
"share_encoder_decoder_embeddings": true,
|
40 |
+
"static_position_embeddings": true,
|
41 |
+
"torch_dtype": "float16",
|
42 |
+
"transformers_version": "4.18.0.dev0",
|
43 |
+
"use_cache": true,
|
44 |
+
"vocab_size": 56728
|
45 |
+
}
|
pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
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|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:db3fcc4f08b00f84532ba66c66511b3be3649a81f34af2516079e8ea7e0dcbe4
|
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size 585277763
|
source.spm
ADDED
@@ -0,0 +1,3 @@
|
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|
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|
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:06a1b553146ea2267fa9c35c807334729e5da52891fed3f4586543db9dd18c37
|
3 |
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size 823934
|
special_tokens_map.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
|
target.spm
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:abe1d5af0e56a45bcd1310b56ea9d5c571caab24e2d319556b3f10e414824fdd
|
3 |
+
size 805921
|
tokenizer_config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"source_lang": "de", "target_lang": "gmq", "unk_token": "<unk>", "eos_token": "</s>", "pad_token": "<pad>", "model_max_length": 512, "sp_model_kwargs": {}, "separate_vocabs": false, "special_tokens_map_file": null, "name_or_path": "marian-models/opusTCv20210807_transformer-big_2022-07-25/de-gmq", "tokenizer_class": "MarianTokenizer"}
|
vocab.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|