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Migrate model card from transformers-repo

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Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/MoseliMotsoehli/TswanaBert/README.md

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+ ---
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+ language: tn
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+ ---
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+
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+ # TswanaBert
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+ Pretrained model on the Tswana language using a masked language modeling (MLM) objective.
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+
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+ ## Model Description.
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+ TswanaBERT is a transformer model pre-trained on a corpus of Setswana in a self-supervised fashion by masking part of the input words and training to predict the masks by using byte-level tokens.
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+
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+ ## Intended uses & limitations
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+ The model can be used for either masked language modeling or next word prediction. It can also be fine-tuned on a specific down-stream NLP application.
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+
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+ #### How to use
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+
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+ ```python
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+ >>> from transformers import pipeline
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+ >>> from transformers import AutoTokenizer, AutoModelWithLMHead
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+
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+ >>> tokenizer = AutoTokenizer.from_pretrained("MoseliMotsoehli/TswanaBert")
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+ >>> model = AutoModelWithLMHead.from_pretrained("MoseliMotsoehli/TswanaBert")
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+ >>> unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
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+ >>> unmasker("Ntshopotse <mask> e godile.")
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+
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+ [{'score': 0.32749542593955994,
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+ 'sequence': '<s>Ntshopotse setse e godile.</s>',
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+ 'token': 538,
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+ 'token_str': 'Ġsetse'},
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+ {'score': 0.060260992497205734,
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+ 'sequence': '<s>Ntshopotse le e godile.</s>',
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+ 'token': 270,
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+ 'token_str': 'Ġle'},
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+ {'score': 0.058460816740989685,
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+ 'sequence': '<s>Ntshopotse bone e godile.</s>',
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+ 'token': 364,
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+ 'token_str': 'Ġbone'},
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+ {'score': 0.05694682151079178,
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+ 'sequence': '<s>Ntshopotse ga e godile.</s>',
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+ 'token': 298,
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+ 'token_str': 'Ġga'},
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+ {'score': 0.0565204992890358,
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+ 'sequence': '<s>Ntshopotse, e godile.</s>',
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+ 'token': 16,
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+ 'token_str': ','}]
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+ ```
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+
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+ #### Limitations and bias
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+ The model is trained on a relatively small collection of setwana, mostly from news articles and creative writtings, and so is not representative enough of the language as yet.
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+
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+ ## Training data
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+
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+ 1. The largest portion of this dataset (10k) sentences of text, comes from the [Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download)
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+
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+ 2. I Then added SABC news headlines collected by Marivate Vukosi, & Sefara Tshephisho, (2020) that is generously made available on [zenoodo](http://doi.org/10.5281/zenodo.3668495 ). This added 185 tswana sentences to my corpus.
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+
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+ 3. I went on to add 300 more sentences by scrapping following news sites and blogs that mosty originate in Botswana. I actively continue to expand the dataset.
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+
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+ * http://setswana.blogspot.com/
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+ * https://omniglot.com/writing/tswana.php
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+ * http://www.dailynews.gov.bw/
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+ * http://www.mmegi.bw/index.php
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+ * https://tsena.co.bw
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+ * http://www.botswana.co.za/Cultural_Issues-travel/botswana-country-guide-en-route.html
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+ * https://www.poemhunter.com/poem/2013-setswana/
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+ https://www.poemhunter.com/poem/ngwana-wa-mosetsana/
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+
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+
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+ ### BibTeX entry and citation info
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+
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+ ```bibtex
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+ @inproceedings{author = {Moseli Motsoehli},
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+ year={2020}
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+ }
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+ ```