Add FARM Framework

FARM makes cutting edge Transfer Learning for NLP simple. It is a home for all species of pretrained language models (e.g. BERT) that can be adapted to different down-stream tasks. The aim is to make it simple to perform document classification, NER and question answering, for example, using the one language model. 

The standardized interfaces for language models and prediction heads allow flexible extension by researchers and easy adaptation for practitioners. Additional experiment tracking and visualizations support you along the way to adapt a SOTA model to your own NLP problem.

Author: tholor

README.md

@@ -147,6 +147,7 @@ Blogs and Newsletters
   - [NLP Architect](https://github.com/NervanaSystems/nlp-architect) - A library for exploring the state-of-the-art deep learning topologies and techniques for NLP and NLU
   - [Flair](https://github.com/zalandoresearch/flair) - A very simple framework for state-of-the-art multilingual NLP built on PyTorch. Includes BERT, ELMo and Flair embeddings.
   - [Kashgari](https://github.com/BrikerMan/Kashgari) - Simple, Keras-powered multilingual NLP framework, allows you to build your models in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS) and text classification tasks. Includes BERT and word2vec embedding.
+  - [FARM](https://github.com/deepset-ai/FARM) - FARM makes cutting-edge transfer learning simple and helps you to leverage pretrained language models for your own NLP tasks.
 
 - <a id="c++">**C++** - C++ Libraries</a> | [Back to Top](#contents)
   - [MIT Information Extraction Toolkit](https://github.com/mit-nlp/MITIE) - C, C++, and Python tools for named entity recognition and relation extraction