This is the code for our KILT leaderboard submission to the T-REx and zsRE tasks. It includes code for training a DPR model then continuing training with RAG.

Last update: Jan 06, 2023

Related tags

Deep Learning kgi-slot-filling

Overview

KGI (Knowledge Graph Induction) for slot filling

This is the code for our KILT leaderboard submission to the T-REx and zsRE tasks. It includes code for training a DPR model then continuing training with RAG.

Our model is described in: Zero-shot Slot Filling with DPR and RAG

Available from Hugging Face as:

Dataset	Type	Model Name	Tokenizer Name
T-REx	DPR (ctx)	michaelrglass/dpr-ctx_encoder-multiset-base-kgi0-trex	facebook/dpr-ctx_encoder-multiset-base
T-REx	RAG	michaelrglass/rag-token-nq-kgi0-trex	rag-token-nq
zsRE	DPR (ctx)	michaelrglass/dpr-ctx_encoder-multiset-base-kgi0-zsre	facebook/dpr-ctx_encoder-multiset-base
zsRE	RAG	michaelrglass/rag-token-nq-kgi0-zsre	rag-token-nq

Process to reproduce

Download the KILT data and knowledge source

Segment the KILT Knowledge Source into passages:

python slot_filling/kilt_passage_corpus.py \
--kilt_corpus kilt_knowledgesource.json --output_dir kilt_passages --passage_ids passage_ids.txt

Generate the first phase of the DPR training data

python dpr/dpr_kilt_slot_filling_dataset.py \
--kilt_data structured_zeroshot-train-kilt.jsonl \
--passage_ids passage_ids.txt \
--output_file zsRE_train_positive_pids.jsonl

python dpr/dpr_kilt_slot_filling_dataset.py \
--kilt_data trex-train-kilt.jsonl \
--passage_ids passage_ids.txt \
--output_file trex_train_positive_pids.jsonl

Download and build Anserini. You will need to have Maven and a Java JDK.

git clone https://github.com/castorini/anserini.git
cd anserini
# to use the 0.4.1 version dprBM25.jar is built for
git checkout 3a60106fdc83473d147218d78ae7dca7c3b6d47c
export JAVA_HOME=your JDK directory
mvn clean package appassembler:assemble

put the title/text into the training instance with hard negatives from BM25

python dpr/anserini_prep.py \
--input kilt_passages \
--output anserini_passages

sh Anserini/target/appassembler/bin/IndexCollection -collection JsonCollection \
-generator LuceneDocumentGenerator -threads 40 -input anserini_passages \
-index anserini_passage_index -storePositions -storeDocvectors -storeRawDocs

export CLASSPATH=jar/dprBM25.jar:Anserini/target/anserini-0.4.1-SNAPSHOT-fatjar.jar
java com.ibm.research.ai.pretraining.retrieval.DPRTrainingData \
-passageIndex anserini_passage_index \
-positivePidData ${dataset}_train_positive_pids.jsonl \
-trainingData ${dataset}_dpr_training_data.jsonl

Train DPR

# multi-gpu is not well supported
export CUDA_VISIBLE_DEVICES=0

python dpr/biencoder_trainer.py \
--train_dir zsRE_dpr_training_data.jsonl \
--output_dir models/DPR/zsRE \
--num_train_epochs 2 \
--num_instances 131610 \
--encoder_gpu_train_limit 32 \
--full_train_batch_size 128 \
--max_grad_norm 1.0 --learning_rate 5e-5

python dpr/biencoder_trainer.py \
--train_dir trex_dpr_training_data.jsonl \
--output_dir models/DPR/trex \
--num_train_epochs 2 \
--num_instances 2207953 \
--encoder_gpu_train_limit 32 \
--full_train_batch_size 128 \
--max_grad_norm 1.0 --learning_rate 5e-5

Put the trained DPR query encoder into the NQ RAG model (dataset = trex, zsRE)

python dpr/prepare_rag_model.py \
--save_dir models/RAG/${dataset}_dpr_rag_init  \
--qry_encoder_path models/DPR/${dataset}/qry_encoder

Encode the passages (dataset = trex, zsRE)

python dpr/index_simple_corpus.py \
--embed 1of2 \
--dpr_ctx_encoder_path models/DPR/${dataset}/ctx_encoder \
--corpus kilt_passages  \
--output_dir kilt_passages_${dataset}

python rag/dpr/index_simple_corpus.py \
--embed 2of2 \
--dpr_ctx_encoder_path models/DPR/${dataset}/ctx_encoder \
--corpus kilt_passages \
--output_dir kilt_passages_${dataset}

Index the passage vectors (dataset = trex, zsRE)

python dpr/faiss_index.py \
--corpus_dir kilt_passages_${dataset} \
--scalar_quantizer 8 \
--output_file kilt_passages_${dataset}/index.faiss

Train RAG

python dataloader/file_splitter.py \
--input trex-train-kilt.jsonl \
--outdirs trex_training \
--file_counts 64

python slot_filling/rag_client_server_train.py \
  --kilt_data trex_training \
  --output models/RAG/trex_dpr_rag \
  --corpus_endpoint kilt_passages_trex \
  --model_name facebook/rag-token-nq \
  --model_path models/RAG/trex_dpr_rag_init \
  --num_instances 500000 --warmup_instances 10000  --num_train_epochs 1 \
  --learning_rate 3e-5 --full_train_batch_size 128 --gradient_accumulation_steps 64


python slot_filling/rag_client_server_train.py \
  --kilt_data structured_zeroshot-train-kilt.jsonl \
  --output models/RAG/zsRE_dpr_rag \
  --corpus_endpoint kilt_passages_zsRE \
  --model_name facebook/rag-token-nq \
  --model_path models/RAG/zsRE_dpr_rag_init \
  --num_instances 147909  --warmup_instances 10000 --num_train_epochs 1 \
  --learning_rate 3e-5 --full_train_batch_size 128 --gradient_accumulation_steps 64

Apply RAG (dev_file = trex-dev-kilt.jsonl, structured_zeroshot-dev-kilt.jsonl)

python slot_filling/rag_client_server_apply.py \
  --kilt_data ${dev_file} \
  --corpus_endpoint kilt_passages_${dataset} \
  --output predictions/${dataset}_dev.jsonl \
  --model_name facebook/rag-token-nq \
  --model_path models/RAG/${dataset}_dpr_rag

python eval/convert_for_kilt_eval.py \
--apply_file predictions/${dataset}_dev.jsonl \
--eval_file predictions/${dataset}_dev_kilt_format.jsonl

Run official evaluation script

# install KILT evaluation scripts
git clone https://github.com/facebookresearch/KILT.git
cd KILT
conda create -n kilt37 -y python=3.7 && conda activate kilt37
pip install -r requirements.txt
export PYTHONPATH=`pwd`

# run evaluation
python kilt/eval_downstream.py predictions/${dataset}_dev_kilt_format.jsonl ${dev_file}

This is the code for our KILT leaderboard submission to the T-REx and zsRE tasks. It includes code for training a DPR model then continuing training with RAG.

Related tags

Overview

KGI (Knowledge Graph Induction) for slot filling

Available from Hugging Face as:

Process to reproduce

Owner

International Business Machines

Making a music video with Wav2CLIP and VQGAN-CLIP

Bu repo SAHI uygulamasını mantığını öğreniyoruz.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

This is the second place solution for : UmojaHack Africa 2022: African Snake Antivenom Binding Challenge

Computer Vision and Pattern Recognition, NUS CS4243, 2022

The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

FAST-RIR: FAST NEURAL DIFFUSE ROOM IMPULSE RESPONSE GENERATOR

A light-weight image labelling tool for Python designed for creating segmentation data sets.

Learning trajectory representations using self-supervision and programmatic supervision.

Code for "Steerable Pyramid Transform Enables Robust Left Ventricle Quantification"

DivNoising is an unsupervised denoising method to generate diverse denoised samples for any noisy input image. This repository contains the code to reproduce the results reported in the paper https://openreview.net/pdf?id=agHLCOBM5jP

R interface to fast.ai

Lacmus is a cross-platform application that helps to find people who are lost in the forest using computer vision and neural networks.

Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals.

Immortal tracker

Source code for Fixed-Point GAN for Cloud Detection

IndoNLI: A Natural Language Inference Dataset for Indonesian

Expressive Power of Invariant and Equivaraint Graph Neural Networks (ICLR 2021)

Generate pixel-style avatars with python.

Repo for CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning