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docs/source/index.rst

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 LM-LSTM-CRF documentation
 =========================
 
+**Check Our New NER Toolkit🚀🚀🚀**
+- **Inference**:
+  - **[LightNER](https://github.com/LiyuanLucasLiu/LightNER)**: inference w. models pre-trained / trained w. *any* following tools, *efficiently*. 
+- **Training**:
+  - **[LD-Net](https://github.com/LiyuanLucasLiu/LD-Net)**: train NER models w. efficient contextualized representations.
+  - **[VanillaNER](https://github.com/LiyuanLucasLiu/Vanilla_NER)**: train vanilla NER models w. pre-trained embedding.
+- **Distant Training**:
+  - **[AutoNER](https://shangjingbo1226.github.io/AutoNER/)**: train NER models w.o. line-by-line annotations and get competitive performance.
+
+--------------------------
+
 This project provides high-performance character-aware sequence labeling tools, including [Training](#usage), [Evaluation](#evaluation) and [Prediction](#prediction). 
 
 Details about LM-LSTM-CRF can be accessed [here](http://arxiv.org/abs/1709.04109), and the implementation is based on the PyTorch library.