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license: apache-2.0 |
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# Glot500 (base-sized model) |
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Glot500 model (Glot500-m) pre-trained on 500+ languages using a masked language modeling (MLM) objective. It was introduced in |
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[this paper]() (ACL 2023) and first released in [this repository](https://github.com/cisnlp/Glot500). |
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## Usage |
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You can use this model directly with a pipeline for masked language modeling: |
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```python |
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>>> from transformers import pipeline |
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>>> unmasker = pipeline('fill-mask', model='cis-lmu/glot500-base') |
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>>> unmasker("Hello I'm a <mask> model.") |
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``` |
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Here is how to use this model to get the features of a given text in PyTorch: |
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```python |
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from transformers import AutoTokenizer, AutoModelForMaskedLM |
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tokenizer = AutoTokenizer.from_pretrained('cis-lmu/glot500-base') |
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model = AutoModelForMaskedLM.from_pretrained("cis-lmu/glot500-base") |
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# prepare input |
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text = "Replace me by any text you'd like." |
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encoded_input = tokenizer(text, return_tensors='pt') |
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# forward pass |
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output = model(**encoded_input) |
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``` |
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### BibTeX entry and citation info |
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```bibtex |
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@inproceedings{imani-etal-2023-glot500, |
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title = "Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages", |
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author = " ImaniGooghari, Ayyoob and Lin, Peiqin and Kargaran, Amir Hossein and Severini, Silvia and Sabet, Masoud Jalili and Kassner, Nora and Ma, Chunlan and Schmid, Helmut and Martins, André and Yvon, François and Sch{\"u}tze, Hinrich", |
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booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics.", |
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year = "2023", |
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} |
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``` |
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