Update README.md

README.md

@@ -8,11 +8,25 @@ tags:
 
 ---
 
-# {MODEL_NAME}
+# keyphrase-mpnet-v1
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+This is a [sentence-transformers](https://www.SBERT.net) model specialized for phrases: It maps phrases to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
-<!--- Describe your model here -->
+This model is based on [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) and further fine-tuned on large-scale keyphrase data with SimCSE.
+
+
+## Citing & Authors
+Paper: [KPEval: Towards Fine-grained Semantic-based Evaluation of Keyphrase Extraction and Generation Systems](https://arxiv.org/abs/2303.15422)
+```
+@article{wu2023kpeval,
+      title={KPEval: Towards Fine-grained Semantic-based Evaluation of Keyphrase Extraction and Generation Systems}, 
+      author={Di Wu and Da Yin and Kai-Wei Chang},
+      year={2023},
+      eprint={2303.15422},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
 
 ## Usage (Sentence-Transformers)
 
@@ -33,8 +47,6 @@ embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
-
-
 ## Usage (HuggingFace Transformers)
 Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
 
@@ -71,15 +83,6 @@ print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
 
-
-
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
-
-
 ## Training
 The model was trained with the parameters:
 
@@ -123,7 +126,3 @@ SentenceTransformer(
   (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
 )
 ```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->