Create README.md (#5422)

* Create README.md

* Update model_cards/MoseliMotsoehli/TswanaBert/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

model_cards/MoseliMotsoehli/TswanaBert/README.md

+---
+language: setswana
+---
+
+# TswanaBert
+
+## Model Description.
+TswanaBERT is a transformers model pretrained on a corpus of Setswana data in a self-supervised fashion by masking part of the input words and training to predict the masks.
+
+## Intended uses & limitations
+The model can  be used for either masked language modeling or next word prediction. it can also be fine-tuned for a specifict application. 
+
+#### How to use
+
+```python
+>>> from transformers import pipeline
+>>> from transformers import AutoTokenizer, AutoModelWithLMHead
+
+>>> tokenizer = AutoTokenizer.from_pretrained("MoseliMotsoehli/TswanaBert")
+>>> model = AutoModelWithLMHead.from_pretrained("MoseliMotsoehli/TswanaBert")
+>>> unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
+>>> unmasker("Ntshopotse <mask> e godile.")
+
+[{'score': 0.32749542593955994,
+  'sequence': '<s>Ntshopotse setse e godile.</s>',
+  'token': 538,
+  'token_str': 'Ġsetse'},
+ {'score': 0.060260992497205734,
+  'sequence': '<s>Ntshopotse le e godile.</s>',
+  'token': 270,
+  'token_str': 'Ġle'},
+ {'score': 0.058460816740989685,
+  'sequence': '<s>Ntshopotse bone e godile.</s>',
+  'token': 364,
+  'token_str': 'Ġbone'},
+ {'score': 0.05694682151079178,
+  'sequence': '<s>Ntshopotse ga e godile.</s>',
+  'token': 298,
+  'token_str': 'Ġga'},
+ {'score': 0.0565204992890358,
+  'sequence': '<s>Ntshopotse, e godile.</s>',
+  'token': 16,
+  'token_str': ','}]
+```
+
+#### Limitations and bias
+The model is trained on a fairly small collection of setwana, mostly from news articles and creative writtings, and so is not representative enough of the language as yet.
+
+## Training data
+
+The largest portion of this dataset (10k)  lines of text, comes from the [Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download)
+
+The I then added 200 more phrases and sentences by scrapping following sites. I continue to expand the dataset
+
+* http://setswana.blogspot.com/
+* https://omniglot.com/writing/tswana.php
+* http://www.dailynews.gov.bw/
+* http://www.mmegi.bw/index.php
+* https://tsena.co.bw
+* http://www.botswana.co.za/Cultural_Issues-travel/botswana-country-guide-en-route.html
+
+## Training procedure
+The model was trained on a google colab Tesla T4 GPU for 200 epochs with a batch size of 64, on 13446 learned tokens.
+Other model training configuration setting can be found [here](https://s3.amazonaws.com/models.huggingface.co/bert/MoseliMotsoehli/TswanaBert/config.json)
+
+### BibTeX entry and citation info
+
+```bibtex
+@inproceedings{author = {Moseli Motsoehli},
+  year={2020}
+}
+```