model update

README.md

@@ -48,11 +48,29 @@ model-index:
       value: 0.6455603400134543
 ---
 
-# Language Models Fine-tuning on Question Generation: `lmqg/mt5-base-ruquad`
+# Model Card of `lmqg/mt5-base-ruquad`
 This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the 
-[lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default).
+[lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
 
 
+Please cite our paper if you use the model ([TBA](TBA)).
+
+```
+
+@inproceedings{ushio-etal-2022-generative,
+    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration: {A} {U}nified {B}enchmark and {E}valuation",
+    author = "Ushio, Asahi  and
+        Alva-Manchego, Fernando and
+        Camacho-Collados, Jose",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, U.A.E.",
+    publisher = "Association for Computational Linguistics",
+}
+
+```
+
 ### Overview
 - **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)   
 - **Language:** ru  
@@ -71,6 +89,7 @@ pipe = pipeline("text2text-generation", model_path)
 
 # Question Generation
 question = pipe('Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.')
+
 ```
 
 ## Evaluation Metrics
@@ -107,4 +126,16 @@ The following hyperparameters were used during fine-tuning:
 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-ruquad/raw/main/trainer_config.json).
 
 ## Citation
-TBA
+
+@inproceedings{ushio-etal-2022-generative,
+    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration: {A} {U}nified {B}enchmark and {E}valuation",
+    author = "Ushio, Asahi  and
+        Alva-Manchego, Fernando and
+        Camacho-Collados, Jose",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, U.A.E.",
+    publisher = "Association for Computational Linguistics",
+}
+