Create README.md

README.md

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+# SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval 
+
+paper available at [https://arxiv.org/pdf/2207.02578](https://arxiv.org/pdf/2207.02578)
+
+code available at [https://github.com/microsoft/unilm/tree/master/simlm](https://github.com/microsoft/unilm/tree/master/simlm)
+
+## Paper abstract
+
+In this paper, we propose SimLM (Similarity matching with Language Model pre-training), a simple yet effective pre-training method for dense passage retrieval. 
+It employs a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training. 
+We use a replaced language modeling objective, which is inspired by ELECTRA, 
+to improve the sample efficiency and reduce the mismatch of the input distribution between pre-training and fine-tuning. 
+SimLM only requires access to unlabeled corpus, and is more broadly applicable when there are no labeled data or queries. 
+We conduct experiments on several large-scale passage retrieval datasets, and show substantial improvements over strong baselines under various settings. 
+Remarkably, SimLM even outperforms multi-vector approaches such as ColBERTv2 which incurs significantly more storage cost.
+
+## Results on MS-MARCO passage ranking task
+
+| Model | dev MRR@10 | dev R@50  | dev R@1k  |  TREC DL 2019 nDCG@10 | TREC DL 2020 nDCG@10  |
+|--|---|---|---|---|---|
+| **SimLM (this model)** | 43.8 |  89.2 | 98.6  | 74.6 | 72.7 |
+
+## Usage
+
+Since we use a listwise loss to train the re-ranker,
+the relevance score is not bounded to a specific numerical range.
+Higher scores mean more relevant between the given query and passage.
+
+Get relevance score from our re-ranker:
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, BatchEncoding, PreTrainedTokenizerFast
+from transformers.modeling_outputs import SequenceClassifierOutput
+
+def encode(tokenizer: PreTrainedTokenizerFast,
+           query: str, passage: str, title: str = '-') -> BatchEncoding:
+    return tokenizer(query,
+                     text_pair='{}: {}'.format(title, passage),
+                     max_length=192,
+                     padding=True,
+                     truncation=True,
+                     return_tensors='pt')
+
+tokenizer = AutoTokenizer.from_pretrained('intfloat/simlm-msmarco-reranker')
+model = AutoModelForSequenceClassification.from_pretrained('intfloat/simlm-msmarco-reranker')
+model.eval()
+
+with torch.no_grad():
+    batch_dict = encode(tokenizer, 'how long is super bowl game', 'The Super Bowl is typically four hours long. The game itself takes about three and a half hours, with a 30 minute halftime show built in.')
+    outputs: SequenceClassifierOutput = model(**batch_dict, return_dict=True)
+    print(outputs.logits[0])
+
+    batch_dict = encode(tokenizer, 'how long is super bowl game', 'The cost of a Super Bowl commercial runs about $5 million for 30 seconds of airtime. But the benefits that the spot can bring to a brand can help to justify the cost.')
+    outputs: SequenceClassifierOutput = model(**batch_dict, return_dict=True)
+    print(outputs.logits[0])
+```
+
+## Citation
+
+```bibtex
+@article{Wang2022SimLMPW,
+  title={SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval},
+  author={Liang Wang and Nan Yang and Xiaolong Huang and Binxing Jiao and Linjun Yang and Daxin Jiang and Rangan Majumder and Furu Wei},
+  journal={ArXiv},
+  year={2022},
+  volume={abs/2207.02578}
+}
+```