AIO2_DPR_baseline | https : //www.nlp.ecei.tohoku.ac.jp/projects/aio/
kandi X-RAY | AIO2_DPR_baseline Summary
kandi X-RAY | AIO2_DPR_baseline Summary
AIO2_DPR_baseline is a Python library. AIO2_DPR_baseline has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
https://www.nlp.ecei.tohoku.ac.jp/projects/aio/
https://www.nlp.ecei.tohoku.ac.jp/projects/aio/
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Support
AIO2_DPR_baseline has a low active ecosystem.
It has 12 star(s) with 0 fork(s). There are 6 watchers for this library.
It had no major release in the last 12 months.
There are 0 open issues and 2 have been closed. On average issues are closed in 31 days. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of AIO2_DPR_baseline is v0.0.1
Quality
AIO2_DPR_baseline has no bugs reported.
Security
AIO2_DPR_baseline has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
AIO2_DPR_baseline does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
Reuse
AIO2_DPR_baseline releases are available to install and integrate.
Build file is available. You can build the component from source.
Installation instructions are not available. Examples and code snippets are available.
Top functions reviewed by kandi - BETA
kandi has reviewed AIO2_DPR_baseline and discovered the below as its top functions. This is intended to give you an instant insight into AIO2_DPR_baseline implemented functionality, and help decide if they suit your requirements.
- Validate average rank
- Generate a list of all gather objects
- Iterate over the data
- Reset the negative ctxs
- Validate the learner
- Create reader input
- Normalize an answer
- Converts the retrieved results to a list of dicts
- Run train
- Validate the average rank
- Create an argument parser
- Adds training parameters to the argument parser
- Run train method
- Validate the dataset
- Create a BiEncoderInput
- Normalizes question
- Get Biencoder components
- Read the questions from a file
- Calculate best match results
- Return n - grams
- Saves results to a file
- Add training parameters to argparse
- Generate passage definitions from a file
- Converts the retrieved results into a list of results
- Validate the NLL
- Create index
- Read ctx file
- Generate the query vectors for the given list of questions
- Generate the ctx vectors
- Index data in DNS
Get all kandi verified functions for this library.
AIO2_DPR_baseline Key Features
No Key Features are available at this moment for AIO2_DPR_baseline.
AIO2_DPR_baseline Examples and Code Snippets
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# 実行例
$ exp_name="baseline"
$ config_file="scripts/configs/retriever_base.json"
$ bash scripts/retriever/train_retriever.sh \
-n $exp_name \
-c $config_file
# 実行結果
$ ls $DIR_DPR/$exp_name/retriever
dpr_biencoder.*.*.pt # モデ
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# 実行例
$ exp_name="baseline"
$ config_file="scripts/configs/reader_base.json"
$ train_file="path/to/retrieved/train/file"
$ dev_file="path/to/retrieved/dev/file"
$ bash scripts/reader/train_reader.sh \
-n $exp_name \
-c $config_file \
-t
Copy
{
"question": "明治時代に西洋から伝わった「テーブル・ターニング」に起源を持つ占いの一種で、50音表などを記入した紙を置き、参加者全員の人差し指をコインに置いて行うのは何でしょう?",
"answers": [
"コックリさん"
],
"positive_ctxs": [
{
"id": 278397,
"title": "コックリさん",
"te
Community Discussions
No Community Discussions are available at this moment for AIO2_DPR_baseline.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install AIO2_DPR_baseline
You can download it from GitHub.
You can use AIO2_DPR_baseline like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use AIO2_DPR_baseline like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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