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/microsoft/MiniLM-L12-H384-uncased/README.md

README.md

@@ -0,0 +1,43 @@
+---
+thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
+tags:
+- text-classification
+license: mit
+---
+
+## MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation
+
+MiniLM is a distilled model from the paper "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://arxiv.org/abs/2002.10957)".
+
+Please find the information about preprocessing, training and full details of the MiniLM in the [original MiniLM repository](https://github.com/microsoft/unilm/blob/master/minilm/).
+
+Please note: This checkpoint can be an inplace substitution for BERT and it needs to be fine-tuned before use!
+
+### English Pre-trained Models
+We release the **uncased** **12**-layer model with **384** hidden size distilled from an in-house pre-trained [UniLM v2](/unilm) model in BERT-Base size.
+
+- MiniLMv1-L12-H384-uncased: 12-layer, 384-hidden, 12-heads, 33M parameters, 2.7x faster than BERT-Base
+
+#### Fine-tuning on NLU tasks
+
+We present the dev results on SQuAD 2.0 and several GLUE benchmark tasks.
+
+| Model                                             | #Param | SQuAD 2.0 | MNLI-m | SST-2 | QNLI | CoLA | RTE  | MRPC | QQP  |
+|---------------------------------------------------|--------|-----------|--------|-------|------|------|------|------|------|
+| [BERT-Base](https://arxiv.org/pdf/1810.04805.pdf) | 109M   | 76.8      | 84.5   | 93.2  | 91.7 | 58.9 | 68.6 | 87.3 | 91.3 |
+| **MiniLM-L12xH384**                               | 33M    | 81.7      | 85.7   | 93.0  | 91.5 | 58.5 | 73.3 | 89.5 | 91.3 |
+
+### Citation
+
+If you find MiniLM useful in your research, please cite the following paper:
+
+``` latex
+@misc{wang2020minilm,
+    title={MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers},
+    author={Wenhui Wang and Furu Wei and Li Dong and Hangbo Bao and Nan Yang and Ming Zhou},
+    year={2020},
+    eprint={2002.10957},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```