Migrate model card from transformers-repo

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/dbmdz/distilbert-base-turkish-cased/README.md

README.md

@@ -0,0 +1,77 @@
+---
+language: tr
+license: mit
+---
+
+# 🤗 + 📚 dbmdz Distilled Turkish BERT model
+
+In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
+Library open sources a (cased) distilled model for Turkish 🎉
+
+# 🇹🇷 DistilBERTurk
+
+DistilBERTurk is a community-driven cased distilled BERT model for Turkish.
+
+DistilBERTurk was trained on 7GB of the original training data that was used
+for training [BERTurk](https://github.com/stefan-it/turkish-bert/tree/master#stats),
+using the cased version of BERTurk as teacher model.
+
+*DistilBERTurk* was trained with the official Hugging Face implementation from
+[here](https://github.com/huggingface/transformers/tree/master/examples/distillation)
+for 5 days on 4 RTX 2080 TI.
+
+More details about distillation can be found in the
+["DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"](https://arxiv.org/abs/1910.01108)
+paper by Sanh et al. (2019).
+
+## Model weights
+
+Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers)
+compatible weights are available. If you need access to TensorFlow checkpoints,
+please raise an issue in the [BERTurk](https://github.com/stefan-it/turkish-bert) repository!
+
+| Model                             | Downloads
+| --------------------------------- | ---------------------------------------------------------------------------------------------------------------
+| `dbmdz/distilbert-base-turkish-cased`   | [`config.json`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/vocab.txt)
+
+## Usage
+
+With Transformers >= 2.3 our DistilBERTurk model can be loaded like:
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("dbmdz/distilbert-base-turkish-cased")
+model = AutoModel.from_pretrained("dbmdz/distilbert-base-turkish-cased")
+```
+
+## Results
+
+For results on PoS tagging or NER tasks, please refer to
+[this repository](https://github.com/stefan-it/turkish-bert).
+
+For PoS tagging, DistilBERTurk outperforms the 24-layer XLM-RoBERTa model.
+
+The overall performance difference between DistilBERTurk and the original
+(teacher) BERTurk model is ~1.18%.
+
+# Huggingface model hub
+
+All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).
+
+# Contact (Bugs, Feedback, Contribution and more)
+
+For questions about our BERT models just open an issue
+[here](https://github.com/dbmdz/berts/issues/new) 🤗
+
+# Acknowledgments
+
+Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) for providing us
+additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing
+us the Turkish NER dataset for evaluation.
+
+Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
+Thanks for providing access to the TFRC ❤️
+
+Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
+it is possible to download both cased and uncased models from their S3 storage 🤗