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#!/bin/bash
# Evaluate natural question test data given Wikipedia embeddings and pretrained
# ICT model or a finetuned model for Natural Question task
# Datasets can be downloaded from the following link:
# https://github.com/facebookresearch/DPR/blob/master/data/download_data.py
EVIDENCE_DATA_DIR=<Specify path of Wikipedia dataset>
EMBEDDING_PATH=<Specify path of the embeddings>
CHECKPOINT_PATH=<Specify path of pretrained ICT model or finetuned model>
QA_FILE=<Path of the natural question dev or test dataset>
python tasks/main.py \
--task RETRIEVER-EVAL \
--tokenizer-type BertWordPieceLowerCase \
--num-layers 12 \
--hidden-size 768 \
--num-attention-heads 12 \
--tensor-model-parallel-size 1 \
--micro-batch-size 128 \
--activations-checkpoint-method uniform \
--seq-length 512 \
--max-position-embeddings 512 \
--load ${CHECKPOINT_PATH} \
--evidence-data-path ${EVIDENCE_DATA_DIR} \
--embedding-path ${EMBEDDING_PATH} \
--retriever-seq-length 256 \
--vocab-file bert-vocab.txt\
--qa-data-test ${QA_FILE} \
--faiss-use-gpu \
--retriever-report-topk-accuracies 1 5 20 100 \
--fp16 \
--indexer-log-interval 1000 \
--indexer-batch-size 128
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