Update README.md

README.md

@@ -8,14 +8,21 @@ tags:
 
 ---
 
-# {MODEL_NAME}
+# bowdpr_wiki_triviaft
 
-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 fine-tuned retriever on the TriviaQA Task (without distillation). We introduce a novel pre-training paradigm, Bag-of-Word Prediction, for dense retrieval. 
+This retriever is initialized from a base-sized pre-trained model, [bowdpr/bowdpr_wiki](https://huggingface.co/bowdpr/bowdpr_wiki). Please refer to our [paper](https://arxiv.org/abs/2401.11248) for detailed pre-training and fine-tuning settings. 
+
+Finetuning on QA datasets involves a two-stage pipeline
+ - s1: BM25 negs
+ - s2: BM25 negs + Mined negatives from s1
 
 <!--- Describe your model here -->
 
 ## Usage (Sentence-Transformers)
 
+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.
+
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
 ```
@@ -28,7 +35,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('bowdpr/bowdpr_wiki_triviaft')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -51,8 +58,8 @@ def cls_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('bowdpr/bowdpr_wiki_triviaft')
+model = AutoModel.from_pretrained('bowdpr/bowdpr_wiki_triviaft')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -69,15 +76,6 @@ 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})
-
-
-
 ## Full Model Architecture
 ```
 SentenceTransformerforCL(
@@ -88,4 +86,17 @@ SentenceTransformerforCL(
 
 ## Citing & Authors
 
+If you are interested in our work, please consider citing our paper.
+
+```
+@misc{ma2024bow_pred,
+      title={Drop your Decoder: Pre-training with Bag-of-Word Prediction for Dense Passage Retrieval}, 
+      author={Guangyuan Ma and Xing Wu and Zijia Lin and Songlin Hu},
+      year={2024},
+      eprint={2401.11248},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR}
+}
+```
+
 <!--- Describe where people can find more information -->