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README.md

@@ -7,10 +7,10 @@ language:
 license: apache-2.0
 library_name: transformers
 base_model:
-- Shitao/RetroMAE
+- RetroMAE
 model_index:
 - name: kpr-retromae
-  results:
+results:
 ---
 
 # Knowledgeable Embedding: kpr-retromae
@@ -21,7 +21,7 @@ model_index:
 
 A key limitation of large language models (LLMs) is their inability to capture less-frequent or up-to-date entity knowledge, often leading to factual inaccuracies and hallucinations. Retrieval-augmented generation (RAG), which incorporates external knowledge through retrieval, is a common approach to mitigate this issue.
 
-Although RAG typically relies on embedding-based retrieval, the embedding models themselves are also based on language models and therefore struggle with queries involving less-frequent entities ([Sciavolino et al., 2021](https://arxiv.org/abs/2109.08535)), often failing to retrieve the crucial knowledge needed to overcome this limitation.
+Although RAG typically relies on embedding-based retrieval, the embedding models themselves are also based on language models and therefore struggle with queries involving less-frequent entities, often failing to retrieve the crucial knowledge needed to overcome this limitation.
 
 **Knowledgeable Embedding** enhances performance on such queries by injecting real-world entity knowledge into embeddings, making them more *knowledgeable*.
 
@@ -39,7 +39,7 @@ For further details, refer to [our paper](https://arxiv.org/abs/2507.03922) or [
 | [knowledgeable-ai/kpr-bge-base-en-v1.5](https://huggingface.co/knowledgeable-ai/kpr-bge-base-en-v1.5) | 112M | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |
 | [knowledgeable-ai/kpr-bge-large-en-v1.5](https://huggingface.co/knowledgeable-ai/kpr-bge-large-en-v1.5) | 340M | [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) |
 
-For practical use, we recommend `knowledgeable-ai/kpr-bge-*`, which significantly outperforms state-of-the-art models on queries involving less-frequent entities while performing comparably on other queries, as reported in [our paper](https://arxiv.org/abs/2507.03922).
+For practical use, we recommend `knowledgeable-ai/kpr-bge-en-*`, which significantly outperforms state-of-the-art models on queries involving less-frequent entities while performing comparably on other queries, as reported in [our paper](https://arxiv.org/abs/2507.03922).
 
 Regarding the model size, we do not count the entity embeddings since they are stored in CPU memory and have a negligible impact on runtime performance. See [this page](https://github.com/knowledgeable-embedding/knowledgeable-embedding/wiki/Internals-of-Knowledgeable-Embedding) for details.
 
@@ -50,7 +50,7 @@ Regarding the model size, we do not count the entity embeddings since they are s
 - Maximum Sequence Length: 512
 - Embedding Dimension: 768
 
-## Usage
+## How to use
 
 This model can be used via [Hugging Face Transformers](https://github.com/huggingface/transformers) or [Sentence Transformers](https://github.com/UKPLab/sentence-transformers):
 
@@ -63,8 +63,8 @@ import torch
 MODEL_NAME_OR_PATH = "knowledgeable-ai/kpr-retromae"
 
 input_texts = [
-  "Who founded Dominican Liberation Party?",
-  "Who owns Mompesson House?"
+    "Who founded Dominican Liberation Party?",
+    "Who owns Mompesson House?"
 ]
 
 # Load model and tokenizer from the Hugging Face Hub
@@ -89,8 +89,8 @@ from sentence_transformers import SentenceTransformer
 MODEL_NAME_OR_PATH = "knowledgeable-ai/kpr-retromae"
 
 input_texts = [
-  "Who founded Dominican Liberation Party?",
-  "Who owns Mompesson House?"
+    "Who founded Dominican Liberation Party?",
+    "Who owns Mompesson House?"
 ]
 
 # Load model from the Hugging Face Hub
@@ -115,14 +115,13 @@ This model is licensed under the Apache License, Version 2.0.
 ## Citation
 
 If you use this model in your research, please cite the following paper:
-
 [Dynamic Injection of Entity Knowledge into Dense Retrievers](https://arxiv.org/abs/2507.03922)
 
 ```bibtex
 @article{yamada2025kpr,
-  title={Dynamic Injection of Entity Knowledge into Dense Retrievers},
-  author={Ikuya Yamada and Ryokan Ri and Takeshi Kojima and Yusuke Iwasawa and Yutaka Matsuo},
-  journal={arXiv preprint arXiv:2507.03922},
-  year={2025}
+title={Dynamic Injection of Entity Knowledge into Dense Retrievers},
+author={Ikuya Yamada and Ryokan Ri and Takeshi Kojima and Yusuke Iwasawa and Yutaka Matsuo},
+journal={arXiv preprint arXiv:2507.03922},
+year={2025}
 }
-```
+```