Migrate model card from transformers-repo

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Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/loodos/electra-base-turkish-64k-uncased-discriminator/README.md

README.md

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+---
+language: tr
+---
+
+# Turkish Language Models with Huggingface's Transformers
+
+As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).
+
+# Turkish ELECTRA-Base-discriminator (uncased/64k)
+
+This is ELECTRA-Base model's discriminator which has the same structure with BERT-Base trained on uncased Turkish dataset. This version has a vocab of size 64k, different from default 32k.
+
+## Usage
+
+Using AutoModelWithLMHead and AutoTokenizer from Transformers, you can import the model as described below.
+
+```python
+from transformers import AutoModel, AutoModelWithLMHead
+
+tokenizer = AutoTokenizer.from_pretrained("loodos/electra-base-turkish-64k-uncased-discriminator", do_lower_case=False)
+
+model = AutoModelWithLMHead.from_pretrained("loodos/electra-base-turkish-64k-uncased-discriminator")
+ 
+normalizer = TextNormalization()
+normalized_text = normalizer.normalize(text, do_lower_case=True, is_turkish=True)
+
+tokenizer.tokenize(normalized_text)
+```
+
+### Notes on Tokenizers
+Currently, Huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are two reasons.
+
+1- Vocabulary and sentence piece model is created with NFC/NFKC normalization but tokenizer uses NFD/NFKD. NFD/NFKD normalization changes text that contains Turkish characters I-ı, İ-i, Ç-ç, Ö-ö, Ş-ş, Ğ-ğ, Ü-ü. This causes wrong tokenization, wrong training and loss of information. Some tokens are never trained.(like "şanlıurfa", "öğün", "çocuk" etc.) NFD/NFKD normalization is not proper for Turkish.
+
+2- Python's default ```string.lower()``` and ```string.upper()``` make the conversions
+
+- "I" and "İ" to 'i'
+- 'i' and 'ı' to 'I'
+
+respectively. However, in Turkish, 'I' and 'İ' are two different letters. 
+
+We opened an [issue](https://github.com/huggingface/transformers/issues/6680) in Huggingface's github repo about this bug. Until it is fixed, in case you want to train your model with uncased data, we provide a simple text normalization module (`TextNormalization()` in the code snippet above) in our [repo](https://github.com/Loodos/turkish-language-models).
+
+
+## Details and Contact
+
+You contact us to ask a question, open an issue or give feedback via our github [repo](https://github.com/Loodos/turkish-language-models).
+
+## Acknowledgments
+
+Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.
+