
# Model description

**XLMR-MaCoCu-is** is a large pre-trained language model trained on **Icelandic** 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-is was trained on 4.4GB of Icelandic text, which is equal to 688M tokens. It was trained for 75,000 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-is")
model = AutoModel.from_pretrained("RVN/XLMR-MaCoCu-is") # PyTorch
model = TFAutoModel.from_pretrained("RVN/XLMR-MaCoCu-is") # Tensorflow
```

# Data

For training, we used all Icelandic data that was present in the monolingual Icelandic [MaCoCu](https://macocu.eu/) corpus. After de-duplicating the data, we were left with a total of 4.4 GB of text, which equals 688M tokens.

# Benchmark performance

We tested the performance of **XLMR-MaCoCu-is** on benchmarks of XPOS, UPOS, NER and COPA. For UPOS and XPOS, we used the data from the [Universal Dependencies](https://universaldependencies.org/) project. For NER, we used the data from the MIM-GOLD-NER data set. For COPA, we automatically translated the English data set by using Google Translate. For details please see our [Github repo](https://github.com/RikVN/COPA). We compare performance to the strong multi-lingual models XLMR-base and XLMR-large, but also the monolingual [IceBERT](https://huggingface.co/vesteinn/IceBERT) model. For details regarding the XPOS/UPOS/NER 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.

|                    | **UPOS** | **UPOS** | **XPOS** | **XPOS** | **NER** | **NER**  | **COPA**  |
|--------------------|:--------:|:--------:|:--------:|:--------:|---------|----------| ----------|
|                    |  **Dev** | **Test** |  **Dev** | **Test** | **Dev** | **Test** | **Test** |
| **XLM-R-base**     |   96.8   |   96.5   |   94.6   |   94.3   |   85.3  |   89.7   | 55.2 |
| **XLM-R-large**    |   97.0   |   96.7   |   94.9   |   94.7   |   88.5  |   91.7   | 54.3 |
| **IceBERT**        |   96.4   |   96.0   |   94.0   |   93.7   |   83.8  |   89.7   | 54.6 | 
| **XLMR-MaCoCu-is** |   **97.3**   |    **97.0**    |   **95.4**   |   **95.1**  |   **90.8**  |   **93.2**   | **59.6** |

# 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": ["is"], "license": "cc0-1.0", "tags": ["MaCoCu"]}
transformers pytorch tf jax safetensors xlm-roberta feature-extraction MaCoCu is license:cc0-1.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #safetensors #xlm-roberta #feature-extraction #MaCoCu #is #license-cc0-1.0 #endpoints_compatible #region-us 

Model description
=================


XLMR-MaCoCu-is is a large pre-trained language model trained on Icelandic 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-is was trained on 4.4GB of Icelandic text, which is equal to 688M tokens. It was trained for 75,000 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 Icelandic data that was present in the monolingual Icelandic MaCoCu corpus. After de-duplicating the data, we were left with a total of 4.4 GB of text, which equals 688M tokens.


Benchmark performance
=====================


We tested the performance of XLMR-MaCoCu-is on benchmarks of XPOS, UPOS, NER and COPA. For UPOS and XPOS, we used the data from the Universal Dependencies project. For NER, we used the data from the MIM-GOLD-NER data set. For COPA, we automatically translated the English data set by using Google Translate. For details please see our Github repo. We compare performance to the strong multi-lingual models XLMR-base and XLMR-large, but also the monolingual IceBERT model. For details regarding the XPOS/UPOS/NER 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.



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 #safetensors #xlm-roberta #feature-extraction #MaCoCu #is #license-cc0-1.0 #endpoints_compatible #region-us 

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