ReInfoSelect

KO | EN

gitlite — search

#javascript #hacktoberfest #react #llm #android #linux #docker #java #windows #kubernetes #vue #pytorch #security #ios

(★ 36)

No description available.

ReInfoSelect Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# ReInfoSelect
Codes and datasets of WWW2020 Paper **Selective Weak Supervision for Neural Information Retrieval**. [Paper](https://arxiv.org/pdf/2001.10382.pdf)

## Framework

![ReInfoSelect](https://raw.githubusercontent.com/thunlp/ReInfoSelect/master/ReInfoSelect.png)

## Results

|Model|Method|ClueWeb09 NDCG@20|ClueWeb09 ERR@20|Robust04 NDCG@20|Robust04 ERR@20|ClueWeb12 NDCG@20|ClueWeb12 ERR@20|
|:----|:----:|:---------------:|:--------------:|:--------------:|:-------------:|:---------------:|:--------------:|
|[Conv-KNRM](http://www.cs.cmu.edu/~zhuyund/papers/WSDM_2018_Dai.pdf)|**No Weak Supervision**|0.2873|0.1597|0.4267|0.1168|0.1123|0.0915|
||**ReInfoSelect**|0.3096|0.1611|0.4423|0.1202|0.1225|***0.1044***|
|[TK](https://arxiv.org/pdf/1912.01385.pdf)|**No Weak Supervision**|0.3003|0.1577|0.4273|0.1163|0.1192|0.0991|
||**ReInfoSelect**|0.3103|0.1626|0.4320|0.1183|***0.1297***|0.1043|
|[EDRM](https://www.aclweb.org/anthology/P18-1223.pdf)|**No Weak Supervision**|0.2922|0.1642|0.4263|0.1158|0.1119|0.0910|
||**ReInfoSelect**|0.3163|***0.1794***|0.4396|0.1208|0.1215|0.0980|
|[BERT](https://arxiv.org/pdf/1810.04805.pdf)|**No Weak Supervision**|0.2999|0.1631|0.4258|0.1163|0.1190|0.0963|
||**ReInfoSelect**|***0.3261***|0.1669|***0.4500***|***0.1220***|0.1276|0.0997|

More results are available in [results](./results).

## Datasets
Data can be downloaded from [Datasets](https://cloud.tsinghua.edu.cn/d/77741ef1c1704866814a/).

|Datasets|Queries/Anchors|Query/Anchor-Doc Pairs|Released Files|
|:-------|:-------------:|:--------------------:|:-------------|
|**Weak Supervision**|100K|6.75M|Anchors, A-D Relations|
|**ClueWeb09-B**|200|47.1K|Queries, Q-D Relations, SDM scores|
|**Robust04**|249|311K|Queries, Q-D Relations, SDM scores|
|**ClueWeb12-B13**|100|28.9K|Queries, Q-D Relations, SDM scores|

As we cannot release the document contents, the document IDs are used instead.

## Requirements

### Setup requirements directly
* `python == 3.7`
* `torch >= 1.0.0`

To run ReInfoSelect, please install all requirements.
```
pip install -r requirements.txt
```

### Use docker

```
cd docker
docker build -t reinfoselect_official:v0.1 .
```

## data
```
wget http://nlp.stanford.edu/data/glove.6B.zip -P ./data
unzip ./data/glove.6B.zip -d ./data
wget https://msmarco.blob.core.windows.net/msmarcoranking/triples.train.small.tar.gz -P ./data
tar -zxvf triples.train.small.tar.gz -C ./data
```

## Run
First, please prepare your data in recommended [format](./data). The MS MARCO or ClueWeb09 corpus may be too large for your machine, you can change *-max_input* to set max input numbers of training instances.

For cknrm training, set *-model* to *cknrm*, for bert training, set to *bert*, the batch\_size for bert may need to set smaller.
```
sh train.sh
```

Then, concatenate the neural features with retrieval (SDM or BM25) score and run Coor-Ascent using [RankLib](https://sourceforge.net/p/lemur/wiki/RankLib/).
```
sh coor_ascent.sh
```

For inference:
```
sh inference.sh
```

For ensemble:
```
sh ensemble.sh
```

## Citation
Please cite our paper if you find it helpful.

```
@inproceedings{Zhang2020SelectiveWS,
    title = {Selective Weak Supervision for Neural Information Retrieval},
    author = {Kaitao Zhang and Chenyan Xiong and Zhenghao Liu and Zhiyuan Liu},
    booktitle = {{WWW} '20: The Web Conference 2020},
    year = {2020}
}
```

## Contact
If you have any questions, suggestions and bug reports, please email zkt18@mails.tsinghua.edu.cn.

ReInfoSelect

ReInfoSelect Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

ReInfoSelect

ReInfoSelect Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README