# 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>

{"language": ["en"]}
transformers pytorch roberta en arxiv:1910.09700 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #en #arxiv-1910.09700 #endpoints_compatible #has_space #region-us 

# 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 
    - Associated Paper 
 
# 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) and Bender et al. (2021)). 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 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 presented in Lacoste et al. (2019).
 
- 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,
 
 
BibTeX:
 

 
 
# 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>


</details>

# 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 
    - Associated Paper # 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) and Bender et al. (2021)). 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 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 presented in Lacoste et al. (2019).
 
- 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,
 
 
BibTeX: # 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>


</details>
TAGS
#transformers #pytorch #roberta #en #arxiv-1910.09700 #endpoints_compatible #has_space #region-us 
 # 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 
    - Associated Paper # 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) and Bender et al. (2021)). 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 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 presented in Lacoste et al. (2019).
 
- 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,
 
 
BibTeX: # 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>


</details>
passage: TAGS
#transformers #pytorch #roberta #en #arxiv-1910.09700 #endpoints_compatible #has_space #region-us 
# 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 
    - Associated Paper# 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) and Bender et al. (2021)). 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 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