Instructions to use Davlan/afro-xlmr-large-29L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Davlan/afro-xlmr-large-29L with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Davlan/afro-xlmr-large-29L")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Davlan/afro-xlmr-large-29L") model = AutoModelForMaskedLM.from_pretrained("Davlan/afro-xlmr-large-29L", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -44,7 +44,7 @@ AfroXLMR-large was created by MLM adaptation of XLM-R-large model on 25 African
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(Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Nigerian-Pidgin, Kinyarwanda,
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Kirundi, Shona, Somali, Sesotho, Swahili, isiXhosa, Yoruba, isiZulu, Ewe, Lingala, Twi,
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Tsonga, Tigrinya, Setswana, Egyptian Arabic, and Northern Sotho)
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covering the major African language families and 4 high
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### Acknowledgment
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We would like to thank Google Cloud for providing us access to TPU v3-8 through the free cloud credits. Model trained using flax, and converted to pytorch.
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(Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Nigerian-Pidgin, Kinyarwanda,
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Kirundi, Shona, Somali, Sesotho, Swahili, isiXhosa, Yoruba, isiZulu, Ewe, Lingala, Twi,
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Tsonga, Tigrinya, Setswana, Egyptian Arabic, and Northern Sotho)
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covering the major African language families and 4 high-resource languages (Arabic, French, and English, Portuguese).
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### Acknowledgment
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We would like to thank Google Cloud for providing us access to TPU v3-8 through the free cloud credits. Model trained using flax, and converted to pytorch.
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