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#!/bin/bash
#########################
# Evaluate the F1 scores.
#########################
WORLD_SIZE=1
DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
--nnodes 1 \
--node_rank 0 \
--master_addr localhost \
--master_port 6000"
MODEL_GEN_PATH=<PATH_OF_THE_KNOWLEDGE_GENERATION> \
(e.g., /testseen_knowledge_generations.txt)
GROUND_TRUTH_PATH=<PATH_OF_THE_GROUND_TRUTH_KNOWLEDGE> \
(e.g., /testseen_knowledge_reference.txt)
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/msdp/main.py \
--num_layers 24 \
--hidden_size 1024 \
--num_attention_heads 16 \
--seq_length 2048 \
--max_position_embeddings 2048 \
--micro_batch_size 4 \
--task MSDP-EVAL-F1 \
--guess_file ${MODEL_GEN_PATH} \
--answer_file ${GROUND_TRUTH_PATH}
############################################
# Evaluate BLEU, METEOR, and ROUGE-L scores.
############################################
# We follow the nlg-eval (https://github.com/Maluuba/nlg-eval) to
# evaluate the BLEU, METEOR, and ROUGE-L scores.
# To evaluate on these metrics, please setup the environments based on
# the nlg-eval github, and run the corresponding evaluation commands.
nlg-eval \
--hypothesis=<PATH_OF_THE_KNOWLEDGE_GENERATION> \
--references=<PATH_OF_THE_GROUND_TRUTH_KNOWLEDGE>
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