Update README.md

README.md

@@ -17,16 +17,16 @@ widget:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# bert-base-cased-PLANE-ood-2
+# BERT for PLANE classification 
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on one of the PLANE's dataset split (no.2), introduced in [Bertolini et al., COLING 2022](https://aclanthology.org/2022.coling-1.359/)
 It achieves the following results on the evaluation set:
-- Loss: 0.9992
+
 - Accuracy: 0.9043
 
 ## Model description
 
-More information needed
+The model is trined to perform a sequence classification task over phrase-level adjective-noun inferences (e.g., "A red car is a vehicle").
 
 ## Intended uses & limitations
 
@@ -34,7 +34,7 @@ More information needed
 
 ## Training and evaluation data
 
-More information needed
+The data used for training and testing, as well as the other splits used for the experiments, are available on the paper's git page [here](https://github.com/lorenzoscottb/PLANE)
 
 ## Training procedure
 
@@ -49,13 +49,29 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 1
 
-### Training results
-
-
-
 ### Framework versions
 
 - Transformers 4.25.1
 - Pytorch 1.12.1
 - Datasets 2.5.1
-- Tokenizers 0.12.1
+- Tokenizers 0.12.1
+
+# Cite
+
+if you want to use the model or data in your work please reference the paper too
+
+```
+@inproceedings{bertolini-etal-2022-testing,
+    title = "Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment",
+    author = "Bertolini, Lorenzo  and
+      Weeds, Julie  and
+      Weir, David",
+    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
+    month = oct,
+    year = "2022",
+    address = "Gyeongju, Republic of Korea",
+    publisher = "International Committee on Computational Linguistics",
+    url = "https://aclanthology.org/2022.coling-1.359",
+    pages = "4084--4100",
+}
+```