The Easy-to-use Dialogue Response Selection Toolkit for Researchers

Overview

Easy-to-use toolkit for retrieval-based Chatbot

Recent Activity

  1. Our released RRS corpus can be found here.
  2. Our released BERT-FP post-training checkpoint for the RRS corpus can be found here.
  3. Our related work (Exploring Dense Retrieval for Dialogue Response Selection) can be found here.

How to Use

  1. Init the repo

    Before using the repo, please run the following command to init:

    # create the necessay folders
    python init.py
    
    # prepare the environment
    # if some package cannot be installed, just google and install it from other ways
    pip install -r requirements.txt
  2. train the model

    ./scripts/train.sh <dataset_name> <model_name> <cuda_ids>
  3. test the model [rerank]

    ./scripts/test_rerank.sh <dataset_name> <model_name> <cuda_id>
  4. test the model [recal]

    # different recall_modes are available: q-q, q-r
    ./scripts/test_recall.sh <dataset_name> <model_name> <cuda_id>
  5. inference the responses and save into the faiss index

    Somethings inference will missing data samples, please use the 1 gpu (faiss-gpu search use 1 gpu quickly)

    It should be noted that: 1. For writer dataset, use extract_inference.py script to generate the inference.txt 2. For other datasets(douban, ecommerce, ubuntu), just cp train.txt inference.txt. The dataloader will automatically read the test.txt to supply the corpus.

    # work_mode=response, inference the response and save into faiss (for q-r matching) [dual-bert/dual-bert-fusion]
    # work_mode=context, inference the context to do q-q matching
    # work_mode=gray, inference the context; read the faiss(work_mode=response has already been done), search the topk hard negative samples; remember to set the BERTDualInferenceContextDataloader in config/base.yaml
    ./scripts/inference.sh <dataset_name> <model_name> <cuda_ids>

    If you want to generate the gray dataset for the dataset:

    # 1. set the mode as the **response**, to generate the response faiss index; corresponding dataset name: BERTDualInferenceDataset;
    ./scripts/inference.sh <dataset_name> response <cuda_ids>
    
    # 2. set the mode as the **gray**, to inference the context in the train.txt and search the top-k candidates as the gray(hard negative) samples; corresponding dataset name: BERTDualInferenceContextDataset
    ./scripts/inference.sh <dataset_name> gray <cuda_ids>
    
    # 3. set the mode as the **gray-one2many** if you want to generate the extra positive samples for each context in the train set, the needings of this mode is the same as the **gray** work mode
    ./scripts/inference.sh <dataset_name> gray-one2many <cuda_ids>

    If you want to generate the pesudo positive pairs, run the following commands:

    # make sure the dual-bert inference dataset name is BERTDualInferenceDataset
    ./scripts/inference.sh <dataset_name> unparallel <cuda_ids>
  6. deploy the rerank and recall model

    # load the model on the cuda:0(can be changed in deploy.sh script)
    ./scripts/deploy.sh <cuda_id>

    at the same time, you can test the deployed model by using:

    # test_mode: recall, rerank, pipeline
    ./scripts/test_api.sh <test_mode> <dataset>
  7. test the recall performance of the elasticsearch

    Before testing the es recall, make sure the es index has been built:

    # recall_mode: q-q/q-r
    ./scripts/build_es_index.sh <dataset_name> <recall_mode>
    # recall_mode: q-q/q-r
    ./scripts/test_es_recall.sh <dataset_name> <recall_mode> 0
  8. simcse generate the gray responses

    # train the simcse model
    ./script/train.sh <dataset_name> simcse <cuda_ids>
    # generate the faiss index, dataset name: BERTSimCSEInferenceDataset
    ./script/inference_response.sh <dataset_name> simcse <cuda_ids>
    # generate the context index
    ./script/inference_simcse_response.sh <dataset_name> simcse <cuda_ids>
    # generate the test set for unlikelyhood-gen dataset
    ./script/inference_simcse_unlikelyhood_response.sh <dataset_name> simcse <cuda_ids>
    # generate the gray response
    ./script/inference_gray_simcse.sh <dataset_name> simcse <cuda_ids>
    # generate the test set for unlikelyhood-gen dataset
    ./script/inference_gray_simcse_unlikelyhood.sh <dataset_name> simcse <cuda_ids>
Owner
GMFTBY
Those who are crazy enough to think they can change the world are the ones who can.
GMFTBY
Awesome Treasure of Transformers Models Collection

💁 Awesome Treasure of Transformers Models for Natural Language processing contains papers, videos, blogs, official repo along with colab Notebooks. 🛫☑️

Ashish Patel 577 Jan 07, 2023
Need: Image Search With Python

Need: Image Search The problem is that a user needs to search for a specific ima

Surya Komandooru 1 Dec 30, 2021
Pytorch-Named-Entity-Recognition-with-BERT

BERT NER Use google BERT to do CoNLL-2003 NER ! Train model using Python and Inference using C++ ALBERT-TF2.0 BERT-NER-TENSORFLOW-2.0 BERT-SQuAD Requi

Kamal Raj 1.1k Dec 25, 2022
🎐 a python library for doing approximate and phonetic matching of strings.

jellyfish Jellyfish is a python library for doing approximate and phonetic matching of strings. Written by James Turk James Turk 1.8k Dec 21, 2022

ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab

AliceMind AliceMind: ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab This repository provides pre-trained encode

Alibaba 1.4k Jan 04, 2023
Which Apple Keeps Which Doctor Away? Colorful Word Representations with Visual Oracles

Which Apple Keeps Which Doctor Away? Colorful Word Representations with Visual Oracles (TASLP 2022)

Zhuosheng Zhang 3 Apr 14, 2022
Code to use Augmented Shapiro Wilks Stopping, as well as code for the paper "Statistically Signifigant Stopping of Neural Network Training"

This codebase is being actively maintained, please create and issue if you have issues using it Basics All data files are included under losses and ea

Justin Terry 32 Nov 09, 2021
Chatbot for the Chatango messaging platform

BroiestBot The baddest bot in the game right now. Uses the ch.py framework for joining Chantango rooms and responding to user messages. Commands If a

Todd Birchard 3 Jan 17, 2022
An open source framework for seq2seq models in PyTorch.

pytorch-seq2seq Documentation This is a framework for sequence-to-sequence (seq2seq) models implemented in PyTorch. The framework has modularized and

International Business Machines 1.4k Jan 02, 2023
A list of NLP(Natural Language Processing) tutorials built on Tensorflow 2.0.

A list of NLP(Natural Language Processing) tutorials built on Tensorflow 2.0.

Won Joon Yoo 335 Jan 04, 2023
Label data using HuggingFace's transformers and automatically get a prediction service

Label Studio for Hugging Face's Transformers Website • Docs • Twitter • Join Slack Community Transfer learning for NLP models by annotating your textu

Heartex 135 Dec 29, 2022
Learn meanings behind words is a key element in NLP. This project concentrates on the disambiguation of preposition senses. Therefore, we train a bert-transformer model and surpass the state-of-the-art.

New State-of-the-Art in Preposition Sense Disambiguation Supervisor: Prof. Dr. Alexander Mehler Alexander Henlein Institutions: Goethe University TTLa

Dirk Neuhäuser 4 Apr 06, 2022
Smart discord chatbot integrated with Dialogflow to manage different classrooms and assist in teaching!

smart-school-chatbot Smart discord chatbot integrated with Dialogflow to interact with students naturally and manage different classes in a school. De

Tom Huynh 5 Oct 24, 2022
Predicting the usefulness of reviews given the review text and metadata surrounding the reviews.

Predicting Yelp Review Quality Table of Contents Introduction Motivation Goal and Central Questions The Data Data Storage and ETL EDA Data Pipeline Da

Jeff Johannsen 3 Nov 27, 2022
Yomichad - a Japanese pop-up dictionary that can display readings and English definitions of Japanese words

Yomichad is a Japanese pop-up dictionary that can display readings and English definitions of Japanese words, kanji, and optionally named entities. It is similar to yomichan, 10ten, and rikaikun in s

Jonas Belouadi 7 Nov 07, 2022
Sequence Modeling with Structured State Spaces

Structured State Spaces for Sequence Modeling This repository provides implementations and experiments for the following papers. S4 Efficiently Modeli

HazyResearch 902 Jan 06, 2023
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.

State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0 🤗 Transformers provides thousands of pretrained models to perform tasks o

Hugging Face 77.3k Jan 03, 2023
A 10000+ hours dataset for Chinese speech recognition

A 10000+ hours dataset for Chinese speech recognition

309 Dec 16, 2022
Fully featured implementation of Routing Transformer

Routing Transformer A fully featured implementation of Routing Transformer. The paper proposes using k-means to route similar queries / keys into the

Phil Wang 246 Jan 02, 2023
Code for ACL 2022 main conference paper "STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation".

STEMM: Self-learning with Speech-Text Manifold Mixup for Speech Translation This is a PyTorch implementation for the ACL 2022 main conference paper ST

ICTNLP 29 Oct 16, 2022