def main():
parser = argparse.ArgumentParser()
add_encoder_params(parser)
add_training_params(parser)
add_tokenizer_params(parser)
add_reader_preprocessing_params(parser)
# reader specific params
parser.add_argument("--max_n_answers", default=10, type=int,
help="Max amount of answer spans to marginalize per singe passage")
parser.add_argument('--passages_per_question', type=int, default=2,
help="Total amount of positive and negative passages per question")
parser.add_argument('--passages_per_question_predict', type=int, default=50,
help="Total amount of positive and negative passages per question for evaluation")
parser.add_argument("--max_answer_length", default=10, type=int,
help="The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another.")
parser.add_argument('--eval_top_docs', nargs='+', type=int,
help="top retrival passages thresholds to analyze prediction results for")
parser.add_argument('--checkpoint_file_name', type=str, default='dpr_reader')
parser.add_argument('--prediction_results_file', type=str, help='path to a file to write prediction results to')
# training parameters
parser.add_argument("--eval_step", default=2000, type=int,
help="batch steps to run validation and save checkpoint")
parser.add_argument("--output_dir", default=None, type=str,
help="The output directory where the model checkpoints will be written to")
parser.add_argument('--fully_resumable', action='store_true',
help="Enables resumable mode by specifying global step dependent random seed before shuffling "
"in-batch data")
args = parser.parse_args()
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
setup_args_gpu(args)
set_seed(args)
print_args(args)
trainer = ReaderTrainer(args)
if args.train_file is not None:
trainer.run_train()
elif args.dev_file:
logger.info("No train files are specified. Run validation.")
trainer.validate()
else:
logger.warning("Neither train_file or (model_file & dev_file) parameters are specified. Nothing to do.")
def setup_reader(model_file):
global reader
parser = argparse.ArgumentParser()
add_encoder_params(parser)
add_training_params(parser)
add_tokenizer_params(parser)
add_reader_preprocessing_params(parser)
args = parser.parse_args()
setup_args_gpu(args)
set_seed(args)
print_args(args)
reader = Reader(args, model_file)
# retrieval specific params
parser.add_argument('--dense_index_path', type=str, default="")
parser.add_argument('--tfidf_path', type=str, default="/checkpoint/sewonmin/dpr/drqa_retrieval_seen_only/db-tfidf-ngram=2-hash=16777216-tokenizer=simple.npz")
parser.add_argument('--match', type=str, default='string', choices=['regex', 'string'])
parser.add_argument('--n-docs', type=int, default=100)
#parser.add_argument('--batch_size', type=int, default=32, help="Batch size for question encoder forward pass")
parser.add_argument('--index_buffer', type=int, default=50000,
help="Temporal memory data buffer size (in samples) for indexer")
parser.add_argument("--hnsw_index", action='store_true', help='If enabled, use inference time efficient HNSW index')
parser.add_argument("--save_or_load_index", action='store_true', default=True, help='If enabled, save index')
# reader specific params
add_encoder_params(parser)
add_training_params(parser)
add_tokenizer_params(parser)
add_reader_preprocessing_params(parser)
parser.add_argument("--max_n_answers", default=10, type=int,
help="Max amount of answer spans to marginalize per singe passage")
parser.add_argument('--passages_per_question', type=int, default=2,
help="Total amount of positive and negative passages per question")
parser.add_argument('--passages_per_question_predict', type=int, default=50,
help="Total amount of positive and negative passages per question for evaluation")
parser.add_argument("--max_answer_length", default=10, type=int,
help="The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another.")
parser.add_argument('--eval_top_docs', nargs='+', type=int,
help="top retrival passages thresholds to analyze prediction results for")
parser.add_argument('--checkpoint_file_name', type=str, default='dpr_reader')
parser.add_argument('--prediction_results_file', type=str, help='path to a file to write prediction results to')
