Update README.md

README.md

@@ -8,7 +8,8 @@ model-index:
 - name: question_answering_model
   results: []
 datasets:
-- squad
+- rajpurkar/squad
+pipeline_tag: question-answering
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -16,21 +17,48 @@ should probably proofread and complete it, then remove this comment. -->
 
 # question_answering_model
 
-This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
+This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on SQuAD.
 It achieves the following results on the evaluation set:
 - Loss: 1.1407
 
 ## Model description
 
-More information needed
+Question and Answering model fine-tuned on SQuAD.
 
 ## Intended uses & limitations
 
-More information needed
+Educational demo of extractive QA with transformers. Not for production, medical, legal, or safety-critical use. 
+
+## Citation Information
+
+```@inproceedings{rajpurkar-etal-2016-squad,
+    title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text",
+    author = "Rajpurkar, Pranav  and
+      Zhang, Jian  and
+      Lopyrev, Konstantin  and
+      Liang, Percy",
+    editor = "Su, Jian  and
+      Duh, Kevin  and
+      Carreras, Xavier",
+    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2016",
+    address = "Austin, Texas",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D16-1264",
+    doi = "10.18653/v1/D16-1264",
+    pages = "2383--2392",
+    eprint={1606.05250},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL},
+}
+
+
 
 ## Training and evaluation data
 
-More information needed
+Trained on [squad](https://huggingface.co/datasets/rajpurkar/squad) (train).
+Evaluated on its validation split.
 
 ## Training procedure