model update

README.md

@@ -53,14 +53,14 @@ This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/
 [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
 This model is fine-tuned without answer information, i.e. generate a question only given a paragraph (note that normal model is fine-tuned to generate a question given a pargraph and an associated answer in the paragraph).
 
-Please cite our paper if you use the model ([TBA](TBA)).
+Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)).
 
 ```
 
 @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",
+    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
     author = "Ushio, Asahi  and
-        Alva-Manchego, Fernando and
+        Alva-Manchego, Fernando  and
         Camacho-Collados, Jose",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
@@ -77,17 +77,27 @@ Please cite our paper if you use the model ([TBA](TBA)).
 - **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
 - **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
 - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
-- **Paper:** [TBA](TBA)
+- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
 
 ### Usage
+- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
 ```python
 
-from transformers import pipeline
+from lmqg import TransformersQG
+# initialize model
+model = TransformersQG(language='en', model='lmqg/bart-base-squad-no-answer')
+# model prediction
+question = model.generate_q(list_context=["William Turner was an English painter who specialised in watercolour landscapes"], list_answer=["William Turner"])
+
+```
 
-model_path = 'lmqg/bart-base-squad-no-answer'
-pipe = pipeline("text2text-generation", model_path)
+- With `transformers`
+```python
 
-# Question Generation
+from transformers import pipeline
+# initialize model
+pipe = pipeline("text2text-generation", 'lmqg/bart-base-squad-no-answer')
+# question generation
 question = pipe('<hl>  Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>')
 
 ```
@@ -126,11 +136,12 @@ The following hyperparameters were used during fine-tuning:
 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-base-squad-no-answer/raw/main/trainer_config.json).
 
 ## Citation
+```
 
 @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",
+    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
     author = "Ushio, Asahi  and
-        Alva-Manchego, Fernando and
+        Alva-Manchego, Fernando  and
         Camacho-Collados, Jose",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
@@ -139,3 +150,4 @@ The full configuration can be found at [fine-tuning config file](https://hugging
     publisher = "Association for Computational Linguistics",
 }
 
+```