# Model description

XLMR-MaCoCu-tr is a large pre-trained language model trained on Turkish texts. It was created by continuing training from the XLM-RoBERTa-large model. It was developed as part of the MaCoCu project and only uses data that was crawled during the project. The main developer is Rik van Noord from the University of Groningen.

XLMR-MaCoCu-tr was trained on 35GB of Turkish text, which is equal to 4.4B tokens. It was trained for 70,000 steps with a batch size of 1,024. It uses the same vocabulary as the original XLMR-large model.

The training and fine-tuning procedures are described in detail on our Github repo.

# How to use

from transformers import AutoTokenizer, AutoModel, TFAutoModel

tokenizer = AutoTokenizer.from_pretrained("RVN/XLMR-MaCoCu-tr")
model = AutoModel.from_pretrained("RVN/XLMR-MaCoCu-tr") # PyTorch
model = TFAutoModel.from_pretrained("RVN/XLMR-MaCoCu-tr") # Tensorflow


# Data

For training, we used all Turkish data that was present in the monolingual Turkish MaCoCu corpus. After de-duplicating the data, we were left with a total of 35 GB of text, which equals 4.4 billion tokens.

# Benchmark performance

We tested the performance of XLMR-MaCoCu-tr on benchmarks of XPOS, UPOS and NER from the Universal Dependencies project. For COPA, we train on a machine translated (MT) set of the data (for details see our Github repo), and evaluate on a similar MT set, but also on the human-translated (HT) test set from the XCOPA project. We compare performance to the strong multi-lingual models XLMR-base and XLMR-large, but also to the monolingual BERTurk model. For details regarding the fine-tuning procedure you can checkout our Github.

Scores are averages of three runs, except for COPA, for which we use 10 runs. We use the same hyperparameter settings for all models for POS/NER, for COPA we optimized each learning rate on the dev set.

UPOS UPOS XPOS XPOS NER NER COPA COPA
Dev Test Dev Test Dev Test Test (MT) Test (HT)
XLM-R-base 89.0 89.0 90.4 90.6 92.8 92.6 56.0 53.2
XLM-R-large 89.4 89.3 90.8 90.7 94.1 94.1 52.1 50.5
BERTurk 88.2 88.4 89.7 89.6 92.6 92.6 57.0 56.4
XLMR-MaCoCu-tr 89.1 89.4 90.7 90.5 94.4 94.4 60.7 58.5

# Acknowledgements

Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC). The authors received funding from the European Union’s Connecting Europe Facility 2014- 2020 - CEF Telecom, under Grant Agreement No.INEA/CEF/ICT/A2020/2278341 (MaCoCu).

# Citation

If you use this model, please cite the following paper:

@inproceedings{non-etal-2022-macocu,
title = "{M}a{C}o{C}u: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages",
author = "Ba{\~n}{\'o}n, Marta  and
Espl{\a}-Gomis, Miquel  and
Garc{\'\i}a-Romero, Cristian  and
Kuzman, Taja  and
Ljube{\v{s}}i{\'c}, Nikola  and
van Noord, Rik  and
Sempere, Leopoldo Pla  and
Ram{\'\i}rez-S{\'a}nchez, Gema  and
Rupnik, Peter  and
Suchomel, V{\'\i}t  and
Toral, Antonio  and
van der Werff, Tobias  and
Zaragoza, Jaume",
booktitle = "Proceedings of the 23rd Annual Conference of the European Association for Machine Translation",
month = jun,
year = "2022",
`