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/amine/bert-base-5lang-cased/README.md

README.md

@@ -0,0 +1,64 @@
+---
+language: 
+- en
+- fr
+- es
+- de
+- zh
+
+tags:
+- pytorch
+- bert
+- multilingual
+- en
+- fr
+- es
+- de
+- zh
+
+datasets: wikipedia
+
+license: apache-2.0
+
+inference: false
+---
+
+# bert-base-5lang-cased
+This is a smaller version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handles only 5 languages (en, fr, es, de and zh) instead of 104.
+The model is therefore 30% smaller than the original one (124M parameters instead of 178M) but gives exactly the same representations for the above cited languages. 
+Starting from `bert-base-5lang-cased` will facilitate the deployment of your model on public cloud platforms while keeping similar results. 
+For instance, Google Cloud Platform requires that the model size on disk should be lower than 500 MB for serveless deployments (Cloud Functions / Cloud ML) which is not the case of the original `bert-base-multilingual-cased`.
+
+For more information about the models size, memory footprint and loading time please refer to the table below:
+
+|            Model             | Num parameters |   Size   |  Memory  | Loading time |
+| ---------------------------- | -------------- | -------- | -------- | ------------ |
+| bert-base-multilingual-cased |   178 million  |  714 MB  | 1400 MB  |    4.2 sec   |
+| bert-base-5lang-cased        |   124 million  |  495 MB  |  950 MB  |    3.6 sec   |
+
+These measurements have been computed on a [Google Cloud n1-standard-1 machine (1 vCPU, 3.75 GB)](https://cloud.google.com/compute/docs/machine-types\#n1_machine_type).
+
+## How to use
+
+```python
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("amine/bert-base-5lang-cased")
+model = AutoModel.from_pretrained("amine/bert-base-5lang-cased")
+
+```
+
+### How to cite
+
+```bibtex
+@inproceedings{smallermbert,
+  title={Load What You Need: Smaller Versions of Mutlilingual BERT},
+  author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
+  booktitle={SustaiNLP / EMNLP},
+  year={2020}
+}
+```
+
+## Contact 
+
+Please contact [email protected] for any question, feedback or request.