added examples/pretrain_ict.sh

examples/pretrain_ict.sh

+#! /bin/bash
+
+# Runs the "217M" parameter biencoder model for ICT retriever
+
+RANK=0
+WORLD_SIZE=1
+
+PRETRAINED_BERT_PATH=<Specify path of pretrained BERT model>
+TEXT_DATA_PATH=<Specify path and file prefix of the text data>
+TITLE_DATA_PATH=<Specify path and file prefix od the titles>
+CHECKPOINT_PATH=<Specify path>
+
+
+python pretrain_ict.py \
+        --num-layers 12 \
+        --hidden-size 768 \
+        --num-attention-heads 12 \
+        --tensor-model-parallel-size 1 \
+        --micro-batch-size 32 \
+        --seq-length 256 \
+        --max-position-embeddings 512 \
+        --train-iters 100000 \
+        --vocab-file bert-vocab.txt \
+        --tokenizer-type BertWordPieceLowerCase \
+	    --DDP-impl torch \
+	    --bert-load ${PRETRAINED_BERT_PATH} \
+        --log-interval 100 \
+	    --eval-interval 1000 \
+	    --eval-iters 10 \
+        --report-topk-accuracies 1 5 10 20 100 \
+	    --retriever-score-scaling \
+        --load $CHECKPOINT_PATH \
+        --save $CHECKPOINT_PATH \
+        --data-path ${TEXT_DATA_PATH} \
+        --titles-data-path ${TITLE_DATA_PATH} \
+        --lr 0.0001 \
+        --lr-decay-style linear \
+        --weight-decay 1e-2 \
+        --clip-grad 1.0 \
+        --lr-warmup-fraction 0.01 \
+        --save-interval 4000 \
+        --exit-interval 8000 \
+        --query-in-block-prob 0.1 \
+        --fp16