--- license: apache-2.0 language: - multilingual datasets: - cis-lmu/Glot500 metrics: - accuracy - f1 - perplexity library_name: transformers pipeline_tag: fill-mask --- # Glot500 (base-sized model) Glot500 model (Glot500-m) pre-trained on 500+ languages using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2305.12182.pdf) (ACL 2023) and first released in [this repository](https://github.com/cisnlp/Glot500). ## Usage You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='cis-lmu/glot500-base') >>> unmasker("Hello I'm a model.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python >>> from transformers import AutoTokenizer, AutoModelForMaskedLM >>> tokenizer = AutoTokenizer.from_pretrained('cis-lmu/glot500-base') >>> model = AutoModelForMaskedLM.from_pretrained("cis-lmu/glot500-base") >>> # prepare input >>> text = "Replace me by any text you'd like." >>> encoded_input = tokenizer(text, return_tensors='pt') >>> # forward pass >>> output = model(**encoded_input) ``` ### BibTeX entry and citation info ```bibtex @article{imanigooghari-etal-2023-glot500, title={Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages}, author={ImaniGooghari, Ayyoob and Lin, Peiqin and Kargaran, Amir Hossein and Severini, Silvia and Jalili Sabet, Masoud and Kassner, Nora and Ma, Chunlan and Schmid, Helmut and Martins, Andr{\'e} and Yvon, Fran{\c{c}}ois and Sch{\"u}tze, Hinrich}, journal={arXiv preprint arXiv:2305.12182}, year={2023} } ```