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README.md
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README.md
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---
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---
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tags:
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tags:
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- translation
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- translation
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license: cc-by-4.0
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---
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---
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### opus-mt-ru-en
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### opus-mt-ru-en
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## Table of Contents
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* source languages: ru
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- [Model Details](#model-details)
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* target languages: en
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- [Uses](#uses)
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* OPUS readme: [ru-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ru-en/README.md)
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- [Risks, Limitations and Biases](#risks-limitations-and-biases)
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- [Training](#training)
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- [Evaluation](#evaluation)
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- [Citation Information](#citation-information)
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- [How to Get Started With the Model](#how-to-get-started-with-the-model)
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## Model Details
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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:** Transformer-align
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- **Language(s):**
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- Source Language: Russian
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- Target Language: English
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- **License:** CC-BY-4.0
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- **Resources for more information:**
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- [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
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## Uses
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#### Direct Use
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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 this section contains 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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Further details about the dataset for this model can be found in the OPUS readme: [ru-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ru-en/README.md)
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## Training
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#### Training Data
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##### Preprocessing
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* Pre-processing: Normalization + SentencePiece
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* Dataset: [opus](https://github.com/Helsinki-NLP/Opus-MT)
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* Download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.zip)
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* Test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.test.txt)
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## Evaluation
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#### Results
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* dataset: opus
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* model: transformer-align
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* pre-processing: normalization + SentencePiece
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* download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.zip)
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* test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.test.txt)
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* test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.eval.txt)
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* test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.eval.txt)
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#### Benchmarks
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## Benchmarks
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| testset | BLEU | chr-F |
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| testset | BLEU | chr-F |
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|-----------------------|-------|-------|
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|-----------------------|-------|-------|
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| newstest2019-ruen.ru.en | 31.4 | 0.576 |
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| newstest2019-ruen.ru.en | 31.4 | 0.576 |
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| Tatoeba.ru.en | 61.1 | 0.736 |
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| Tatoeba.ru.en | 61.1 | 0.736 |
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## Citation Information
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```bibtex
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@InProceedings{TiedemannThottingal:EAMT2020,
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author = {J{\"o}rg Tiedemann and Santhosh Thottingal},
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title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld},
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booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)},
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year = {2020},
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address = {Lisbon, Portugal}
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}
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```
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## How to Get Started With the Model
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```python
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ru-en")
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model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-ru-en")
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```
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{
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{
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"_name_or_path": "/tmp/Helsinki-NLP/opus-mt-ru-en",
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"_num_labels": 3,
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"_num_labels": 3,
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"activation_dropout": 0.0,
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"activation_dropout": 0.0,
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"activation_function": "swish",
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"activation_function": "swish",
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],
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],
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"bos_token_id": 0,
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"bos_token_id": 0,
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"classif_dropout": 0.0,
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"classif_dropout": 0.0,
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"classifier_dropout": 0.0,
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"d_model": 512,
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"d_model": 512,
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"decoder_attention_heads": 8,
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"decoder_attention_heads": 8,
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"decoder_ffn_dim": 2048,
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"decoder_ffn_dim": 2048,
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"decoder_layerdrop": 0.0,
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"decoder_layerdrop": 0.0,
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"decoder_layers": 6,
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"decoder_layers": 6,
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"decoder_start_token_id": 62517,
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"decoder_start_token_id": 62517,
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"decoder_vocab_size": 62518,
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"dropout": 0.1,
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"dropout": 0.1,
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"encoder_attention_heads": 8,
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"encoder_attention_heads": 8,
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"encoder_ffn_dim": 2048,
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"encoder_ffn_dim": 2048,
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"encoder_layerdrop": 0.0,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 6,
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"encoder_layers": 6,
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"eos_token_id": 0,
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"eos_token_id": 0,
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"forced_eos_token_id": 0,
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"id2label": {
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"id2label": {
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"0": "LABEL_0",
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"0": "LABEL_0",
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"1": "LABEL_1",
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"1": "LABEL_1",
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"num_hidden_layers": 6,
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"num_hidden_layers": 6,
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"pad_token_id": 62517,
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"pad_token_id": 62517,
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"scale_embedding": true,
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"scale_embedding": true,
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"share_encoder_decoder_embeddings": true,
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"static_position_embeddings": true,
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"static_position_embeddings": true,
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"transformers_version": "4.22.0.dev0",
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"use_cache": true,
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"vocab_size": 62518
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"vocab_size": 62518
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}
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}
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{
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"_from_model_config": true,
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"bad_words_ids": [
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[
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62517
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]
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],
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"bos_token_id": 0,
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"decoder_start_token_id": 62517,
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"eos_token_id": 0,
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"forced_eos_token_id": 0,
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"max_length": 512,
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"num_beams": 6,
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"pad_token_id": 62517,
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"transformers_version": "4.27.0.dev0"
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}
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