
# 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](https://huggingface.co/xlm-roberta-large) model. It was developed as part of the [MaCoCu](https://macocu.eu/) project and only uses data that was crawled during the project. The main developer is [Rik van Noord](https://www.rikvannoord.nl/) 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](https://github.com/macocu/LanguageModels).

# How to use

```python
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](https://macocu.eu/) 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](https://universaldependencies.org/) project. For COPA, we train on a machine translated (MT) set of the data (for details see our [Github repo](https://github.com/RikVN/COPA)), and evaluate on a similar MT set, but also on the human-translated (HT) test set from the [XCOPA](https://github.com/cambridgeltl/xcopa) project. We compare performance to the strong multi-lingual models XLMR-base and XLMR-large, but also to the monolingual [BERTurk](https://huggingface.co/dbmdz/bert-base-turkish-cased) model. For details regarding the fine-tuning procedure you can checkout our [Github](https://github.com/macocu/LanguageModels).

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:

```bibtex
@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
      Forcada, Mikel L.  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",
    address = "Ghent, Belgium",
    publisher = "European Association for Machine Translation",
    url = "https://aclanthology.org/2022.eamt-1.41",
    pages = "303--304"
}
```
{"language": ["tr"], "license": "cc0-1.0", "tags": ["MaCoCu"]}
transformers pytorch tf jax xlm-roberta feature-extraction MaCoCu tr license:cc0-1.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #xlm-roberta #feature-extraction #MaCoCu #tr #license-cc0-1.0 #endpoints_compatible #region-us 

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
==========


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.



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).


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



TAGS
#transformers #pytorch #tf #jax #xlm-roberta #feature-extraction #MaCoCu #tr #license-cc0-1.0 #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #tf #jax #xlm-roberta #feature-extraction #MaCoCu #tr #license-cc0-1.0 #endpoints_compatible #region-us 
