Update README.md

README.md

@@ -1,199 +1,145 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+datasets:
+- google-research-datasets/natural_questions
+base_model:
+- google-bert/bert-base-uncased
 ---
 
-# Model Card for Model ID
+# svdr-nq
 
-<!-- Provide a quick summary of what the model is/does. -->
+Semi-Parametric Retrieval via Binary Token Index.  Jiawei Zhou, Li Dong, Furu Wei, Lei Chen, arXiv 2024
 
+The model is BERT-based with 12 layers and an embedding size of 20,523, derived from the BERT vocabulary of 30,522 with 999 unused tokens excluded.
 
 
-## Model Details
+## Quick Start
 
-### Model Description
+Download and install `vsearch` repo:
 
-<!-- Provide a longer summary of what this model is. -->
+```
+git clone git@github.com:jzhoubu/vsearch.git
+poetry install
+poetry shell
+```
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+Below is an example to encode queries and passages and compute similarity.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+```python
+import torch
+from src.ir import Retriever
 
-### Model Sources [optional]
+query = "Who first proposed the theory of relativity?"
+passages = [
+    "Albert Einstein (14 March 1879 – 18 April 1955) was a German-born theoretical physicist who is widely held to be one of the greatest and most influential scientists of all time. He is best known for developing the theory of relativity.",
+    "Sir Isaac Newton FRS (25 December 1642 – 20 March 1727) was an English polymath active as a mathematician, physicist, astronomer, alchemist, theologian, and author who was described in his time as a natural philosopher.",
+    "Nikola Tesla (10 July 1856 – 7 January 1943) was a Serbian-American inventor, electrical engineer, mechanical engineer, and futurist. He is known for his contributions to the design of the modern alternating current (AC) electricity supply system."
+]
 
-<!-- Provide the basic links for the model. -->
+ir = Retriever.from_pretrained("vsearch/svdr-nq")
+ir = ir.to("cuda")
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+# Embed the query and passages
+q_emb = ir.encoder_q.embed(query)  # Shape: [1, V]
+p_emb = ir.encoder_p.embed(passages)  # Shape: [4, V]
 
-## Uses
+scores = q_emb @ p_emb.t()
+print(scores)
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+# Output: 
+tensor([[61.5432, 10.3108,  8.6709]], device='cuda:0')
+```
 
-### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## Building Embedding-based Index for Search
 
-[More Information Needed]
+Below are examples to build index for large-scale retrieval
 
-### Downstream Use [optional]
+```python
+# Build the sparse index for the passages
+ir.build_index(passages, index_type="sparse")
+print(ir.index)
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+# Output:
+# Index Type      : SparseIndex
+# Vector Type     : torch.sparse_csr
+# Vector Shape    : torch.Size([3, 29523])
+# Vector Device   : cuda:0
+# Number of Texts : 3
 
-[More Information Needed]
+# Save the index to disk
+index_file = "/path/to/index.npz"
+ir.save_index(path)
 
-### Out-of-Scope Use
+# Load the index from disk
+index_file = "/path/to/index.npz"
+data_file = "/path/to/texts.jsonl"
+ir.load_index(index_file=index_file, data_file=data_file)
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+# Search top-k results for queries
+queries = [query]
+results = ir.retrieve(queries, k=3)
+print(results)
 
-[More Information Needed]
+# Output:
+# SearchResults(
+#   ids=tensor([[0, 1, 2]], device='cuda:0'),
+#   scores=tensor([[97.2458, 39.7507, 37.6407]], device='cuda:0')
+# )
 
-## Bias, Risks, and Limitations
+query_id = 0
+top1_psg_id = results.ids[query_id][0]
+top1_psg = ir.index.get_sample(top1_psg_id)
+print(top1_psg)
+# Output:
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+# Albert Einstein (14 March 1879 – 18 April 1955) was a German-born theoretical physicist who is widely held to be one of the greatest and most influential scientists of all time. He is best known for developing the theory of relativity.
 
-[More Information Needed]
+```
 
-### Recommendations
+## Building Bag-of-token Index for Search
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Our framework supports using tokenization as an index (i.e., a bag-of-token index), which operates on CPU and reduces indexing time and storage requirements by over 90%, compare to an embedding-based index.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+```python
+# Build the bag-of-token index for the passages
+ir.build_index(passages, index_type="bag_of_token")
+print(ir.index)
 
-## How to Get Started with the Model
+# Output:
+# Index Type      : BoTIndex
+# Vector Type     : torch.sparse_csr
+# Vector Shape    : torch.Size([3, 29523])
+# Vector Device   : cuda:0
+# Number of Texts : 3
 
-Use the code below to get started with the model.
+# Search top-k results from bag-of-token index, and embed and rerank them on-the-fly
+queries = [query]
+results = ir.retrieve(queries, k=3, rerank=True)
+print(results)
 
-[More Information Needed]
+# Output:
+# SearchResults(
+#   ids=tensor([0, 2, 1], device='cuda:3'), 
+#   scores=tensor([97.2964, 39.7844, 37.6955], device='cuda:0')
+# )
+```
 
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
 
+## Training Details
 
+Please refer to our paper at [https://arxiv.org/pdf/2405.01924](https://arxiv.org/pdf/2405.01924).
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
 
-## Model Card Contact
 
-[More Information Needed]
+## Citation
+If you find our paper or models helpful, please consider cite as follows:
+```
+@article{zhou2024semi,
+  title={Semi-Parametric Retrieval via Binary Token Index},
+  author={Zhou, Jiawei and Dong, Li and Wei, Furu and Chen, Lei},
+  journal={arXiv preprint arXiv:2405.01924},
+  year={2024}
+}
+```