
# Model Card for FEVER Question Encoder in Re2G
 
# Model Details

> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. 

<img src="https://github.com/IBM/kgi-slot-filling/raw/re2g/model_cards/Re2G_Arch2.png" width="100%"> 

## Training, Evaluation and Inference
The code for training, evaluation and inference is in our github in the [re2g branch](https://github.com/IBM/kgi-slot-filling/tree/re2g).

## Usage

The best way to use the model is by adapting the [dpr_apply.py](https://github.com/IBM/kgi-slot-filling/blob/re2g/dpr/dpr_apply.py)

## Citation
```
@inproceedings{glass-etal-2022-re2g,
    title = "{R}e2{G}: Retrieve, Rerank, Generate",
    author = "Glass, Michael  and
      Rossiello, Gaetano  and
      Chowdhury, Md Faisal Mahbub  and
      Naik, Ankita  and
      Cai, Pengshan  and
      Gliozzo, Alfio",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.194",
    doi = "10.18653/v1/2022.naacl-main.194",
    pages = "2701--2715",
    abstract = "As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements. Recent models such as RAG and REALM have introduced retrieval into conditional generation. These models incorporate neural initial retrieval from a corpus of passages. We build on this line of research, proposing Re2G, which combines both neural initial retrieval and reranking into a BART-based sequence-to-sequence generation. Our reranking approach also permits merging retrieval results from sources with incomparable scores, enabling an ensemble of BM25 and neural initial retrieval. To train our system end-to-end, we introduce a novel variation of knowledge distillation to train the initial retrieval, reranker and generation using only ground truth on the target sequence output. We find large gains in four diverse tasks: zero-shot slot filling, question answering, fact checking and dialog, with relative gains of 9{\%} to 34{\%} over the previous state-of-the-art on the KILT leaderboard. We make our code available as open source.",
}
```
 
## Model Description
The model creators note in the [associated paper](https://aclanthology.org/2022.naacl-main.194.pdf):
> As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger.  However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements.  Recent models such as RAG and REALM have introduced retrieval into conditional generation.  These models incorporate neural initial retrieval from a corpus of passages.  We build on this line of research, proposing Re2G, which combines both neural initial retrieval and reranking into a BART-based sequence-to-sequence generation.  Our reranking approach also permits merging retrieval results from sources with incomparable scores, enabling an ensemble of BM25 and neural initial retrieval.  To train our system end-to-end, we introduce a novel variation of knowledge distillation to train the initial retrieval, reranker and generation using only ground truth on the target sequence output.  We find large gains in four diverse tasks: zero-shot slot filling, question answering, fact checking and dialog, with relative gains of 9% to 34% over the previous state-of-the-art on the KILT leaderboard.  We make our code available as open source.

- **Developed by:** IBM
- **Shared by [Optional]:** IBM

- **Model type:** Query/Passage Reranker
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Parent Model:** [dpr-question_encoder-multiset-base](https://huggingface.co/facebook/dpr-question_encoder-multiset-base)
- **Resources for more information:** 
  - [GitHub Repo](https://github.com/IBM/kgi-slot-filling)
   - [Associated Paper](https://aclanthology.org/2022.naacl-main.194.pdf)


# Uses
 

## Direct Use
This model can be used for the task of encoding a question to a vector to be used as a query into an Approximate Nearest Neighbors index.  It must be used in combination with a context encoder that encodes passages to a vector and indexes them. 


# Citation

 
**BibTeX:**

```bibtex
@inproceedings{glass-etal-2022-re2g,
    title = "{R}e2{G}: Retrieve, Rerank, Generate",
    author = "Glass, Michael  and
      Rossiello, Gaetano  and
      Chowdhury, Md Faisal Mahbub  and
      Naik, Ankita  and
      Cai, Pengshan  and
      Gliozzo, Alfio",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.194",
    doi = "10.18653/v1/2022.naacl-main.194",
    pages = "2701--2715",
    abstract = "As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements. Recent models such as RAG and REALM have introduced retrieval into conditional generation. These models incorporate neural initial retrieval from a corpus of passages. We build on this line of research, proposing Re2G, which combines both neural initial retrieval and reranking into a BART-based sequence-to-sequence generation. Our reranking approach also permits merging retrieval results from sources with incomparable scores, enabling an ensemble of BM25 and neural initial retrieval. To train our system end-to-end, we introduce a novel variation of knowledge distillation to train the initial retrieval, reranker and generation using only ground truth on the target sequence output. We find large gains in four diverse tasks: zero-shot slot filling, question answering, fact checking and dialog, with relative gains of 9{\%} to 34{\%} over the previous state-of-the-art on the KILT leaderboard. We make our code available as open source.",
}
 
```

{"license": "apache-2.0", "tags": ["information retrieval", "reranking"]}
transformers pytorch dpr feature-extraction information retrieval reranking license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #dpr #feature-extraction #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us 


# Model Card for FEVER Question Encoder in Re2G
 
# Model Details

> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. 

<img src="URL width="100%"> 

## Training, Evaluation and Inference
The code for training, evaluation and inference is in our github in the re2g branch.

## Usage

The best way to use the model is by adapting the dpr_apply.py

## Model Description
The model creators note in the associated paper:
> As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger.  However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements.  Recent models such as RAG and REALM have introduced retrieval into conditional generation.  These models incorporate neural initial retrieval from a corpus of passages.  We build on this line of research, proposing Re2G, which combines both neural initial retrieval and reranking into a BART-based sequence-to-sequence generation.  Our reranking approach also permits merging retrieval results from sources with incomparable scores, enabling an ensemble of BM25 and neural initial retrieval.  To train our system end-to-end, we introduce a novel variation of knowledge distillation to train the initial retrieval, reranker and generation using only ground truth on the target sequence output.  We find large gains in four diverse tasks: zero-shot slot filling, question answering, fact checking and dialog, with relative gains of 9% to 34% over the previous state-of-the-art on the KILT leaderboard.  We make our code available as open source.

- Developed by: IBM
- Shared by [Optional]: IBM

- Model type: Query/Passage Reranker
- Language(s) (NLP): English
- License: Apache 2.0
- Parent Model: dpr-question_encoder-multiset-base
- Resources for more information: 
  - GitHub Repo
   - Associated Paper


# Uses
 

## Direct Use
This model can be used for the task of encoding a question to a vector to be used as a query into an Approximate Nearest Neighbors index.  It must be used in combination with a context encoder that encodes passages to a vector and indexes them. 


BibTeX:



# Model Card for FEVER Question Encoder in Re2G # Model Details

> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. 

<img src="URL width="100%"> ## Training, Evaluation and Inference
The code for training, evaluation and inference is in our github in the re2g branch. ## Usage

The best way to use the model is by adapting the dpr_apply.py ## Model Description
The model creators note in the associated paper:
> As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger.  However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements.  Recent models such as RAG and REALM have introduced retrieval into conditional generation.  These models incorporate neural initial retrieval from a corpus of passages.  We build on this line of research, proposing Re2G, which combines both neural initial retrieval and reranking into a BART-based sequence-to-sequence generation.  Our reranking approach also permits merging retrieval results from sources with incomparable scores, enabling an ensemble of BM25 and neural initial retrieval.  To train our system end-to-end, we introduce a novel variation of knowledge distillation to train the initial retrieval, reranker and generation using only ground truth on the target sequence output.  We find large gains in four diverse tasks: zero-shot slot filling, question answering, fact checking and dialog, with relative gains of 9% to 34% over the previous state-of-the-art on the KILT leaderboard.  We make our code available as open source.

- Developed by: IBM
- Shared by [Optional]: IBM

- Model type: Query/Passage Reranker
- Language(s) (NLP): English
- License: Apache 2.0
- Parent Model: dpr-question_encoder-multiset-base
- Resources for more information: 
  - GitHub Repo
   - Associated Paper # Uses ## Direct Use
This model can be used for the task of encoding a question to a vector to be used as a query into an Approximate Nearest Neighbors index.  It must be used in combination with a context encoder that encodes passages to a vector and indexes them. 


BibTeX:
TAGS
#transformers #pytorch #dpr #feature-extraction #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us 
 # Model Card for FEVER Question Encoder in Re2G # Model Details

> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. 

<img src="URL width="100%"> ## Training, Evaluation and Inference
The code for training, evaluation and inference is in our github in the re2g branch. ## Usage

The best way to use the model is by adapting the dpr_apply.py ## Model Description
The model creators note in the associated paper:
> As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger.  However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements.  Recent models such as RAG and REALM have introduced retrieval into conditional generation.  These models incorporate neural initial retrieval from a corpus of passages.  We build on this line of research, proposing Re2G, which combines both neural initial retrieval and reranking into a BART-based sequence-to-sequence generation.  Our reranking approach also permits merging retrieval results from sources with incomparable scores, enabling an ensemble of BM25 and neural initial retrieval.  To train our system end-to-end, we introduce a novel variation of knowledge distillation to train the initial retrieval, reranker and generation using only ground truth on the target sequence output.  We find large gains in four diverse tasks: zero-shot slot filling, question answering, fact checking and dialog, with relative gains of 9% to 34% over the previous state-of-the-art on the KILT leaderboard.  We make our code available as open source.

- Developed by: IBM
- Shared by [Optional]: IBM

- Model type: Query/Passage Reranker
- Language(s) (NLP): English
- License: Apache 2.0
- Parent Model: dpr-question_encoder-multiset-base
- Resources for more information: 
  - GitHub Repo
   - Associated Paper # Uses ## Direct Use
This model can be used for the task of encoding a question to a vector to be used as a query into an Approximate Nearest Neighbors index.  It must be used in combination with a context encoder that encodes passages to a vector and indexes them. 


BibTeX:
passage: TAGS
#transformers #pytorch #dpr #feature-extraction #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us 
# Model Card for FEVER Question Encoder in Re2G# Model Details

> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. 

<img src="URL width="100%">## Training, Evaluation and Inference
The code for training, evaluation and inference is in our github in the re2g branch.## Usage

The best way to use the model is by adapting the dpr_apply.py