model documentation

README.md

@@ -1,4 +1,175 @@
-This model is converted from the original ANCE [repo](https://github.com/microsoft/ANCE) and fitted into Pyserini:
-> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://arxiv.org/pdf/2007.00808.pdf)
+---
+language:
+- en
 
-For more details on how to use it, check our experiments in [Pyserini](https://github.com/castorini/pyserini/blob/master/docs/experiments-ance.md)
+---
+# Model Card for ance-msmarco-passage
+ 
+ 
+Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations. 
+ 
+# Model Details
+ 
+## Model Description
+ 
+Pyserini is primarily designed to provide effective, reproducible, and easy-to-use first-stage retrieval in a multi-stage ranking architecture
+ 
+- **Developed by:** Castorini
+- **Shared by [Optional]:** Hugging Face
+- **Model type:** Information retrieval
+- **Language(s) (NLP):** en
+- **License:** More information needed
+- **Related Models:** More information needed
+  - **Parent Model:** RoBERTa
+- **Resources for more information:** 
+    - [GitHub Repo](https://github.com/castorini/pyserini) 
+    - [Associated Paper](https://dl.acm.org/doi/pdf/10.1145/3404835.3463238) 
+ 
+# Uses
+ 
+ 
+## Direct Use
+ 
+More information needed
+ 
+## Downstream Use [Optional]
+ 
+More information needed
+ 
+## Out-of-Scope Use
+ 
+More information needed
+ 
+# Bias, Risks, and Limitations
+ 
+ 
+Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
+ 
+ 
+## Recommendations
+ 
+ 
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+ 
+ 
+# Training Details
+ 
+## Training Data
+ 
+More information needed
+ 
+## Training Procedure
+ 
+ 
+ 
+### Preprocessing
+ 
+More information needed
+ 
+### Speeds, Sizes, Times
+ 
+More information needed
+ 
+# Evaluation
+ 
+ 
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+The model creators note in the  [associated Paper](https://dl.acm.org/doi/pdf/10.1145/3404835.3463238) that:
+> bag-of-words ranking with BM25 (the default ranking model) on the MS MARCO passage corpus (comprising 8.8M passages)
+ 
+ 
+### Factors
+ 
+More information needed
+ 
+### Metrics
+ 
+More information needed
+ 
+## Results 
+ 
+More information needed
+ 
+# Model Examination
+ 
+More information needed
+ 
+# Environmental Impact
+ 
+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
+ 
+For bag-of-words sparse retrieval, we have built in Anserini (written in Java) custom parsers and ingestion pipelines for common document formats used in IR research,
+ 
+ 
+# Citation
+ 
+ 
+**BibTeX:**
+ 
+```bibtex
+ 
+@INPROCEEDINGS{Lin_etal_SIGIR2021_Pyserini,
+   author = "Jimmy Lin and Xueguang Ma and Sheng-Chieh Lin and Jheng-Hong Yang and Ronak Pradeep and Rodrigo Nogueira",
+   title = "{Pyserini}: A {Python} Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations",
+   booktitle = "Proceedings of the 44th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021)",
+   year = 2021,
+   pages = "2356--2362",
+}
+```
+ 
+ 
+# Glossary [optional]
+ 
+More information needed
+ 
+# More Information [optional]
+ 
+More information needed
+ 
+# Model Card Authors [optional]
+ 
+Castorini in collaboration with Ezi Ozoani and the Hugging Face team.
+ 
+# Model Card Contact
+ 
+More information needed
+ 
+# How to Get Started with the Model
+ 
+Use the code below to get started with the model.
+<details>
+<summary> Click to expand </summary>
+
+```python
+from transformers import AutoTokenizer, AnceEncoder
+ 
+tokenizer = AutoTokenizer.from_pretrained("castorini/ance-msmarco-passage")
+ 
+model = AnceEncoder.from_pretrained("castorini/ance-msmarco-passage")
+ ```
+</details>