Update README.md

README.md

@@ -12,20 +12,3 @@ Models released from [T-NER](https://github.com/asahi417/tner)
 
 ## Reference
 If you use the models released from T-NER, please cite the following [paper](https://aclanthology.org/2021.eacl-demos.7/):
-
-
-```
-@inproceedings{ushio-camacho-collados-2021-ner,
-    title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
-    author = "Ushio, Asahi  and
-      Camacho-Collados, Jose",
-    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
-    month = apr,
-    year = "2021",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2021.eacl-demos.7",
-    pages = "53--62",
-    abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
-}
-```