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csv_file.to_csv(f'RESULTS/{crystal_property}_results.csv') |
# <FILESEP> |
import os |
import random |
import collections |
import tensorflow.compat.v1 as tf |
import numpy as np |
from bert import tokenization |
from utils import file_operation, relevance_info |
from utils.fold_config import FOLD_CONFIG_DICT |
random.seed(118) |
tf.random.set_random_seed(118) |
flags = tf.flags |
FLAGS = flags.FLAGS |
## Required parameters |
flags.DEFINE_string( |
"trec_run_filename", None, |
"where the trec run file (e.g. produced by BM25) is" |
) |
flags.DEFINE_string( |
"qrels_filename", None, |
"where the qrels file is" |
) |
flags.DEFINE_string( |
"query_field", 'title', |
"None if no field, else title, desc, narr, question") |
flags.DEFINE_string( |
"query_filename", None, |
"where the query file is. support TREC file now") |
flags.DEFINE_string( |
"corpus_filename", None, |
"where the corpus file is. format: docno \t content") |
flags.DEFINE_string( |
"dataset", None, |
"which dataset to run on. it would correspond to the fold config of qids" |
) |
flags.DEFINE_integer( |
"fold", 3, |
"run fold") |
flags.DEFINE_integer( |
"plen", 150, |
"length of segmented passage" |
) |
flags.DEFINE_integer( |
"overlap", 50, |
"overlap between continuous segmented passages" |
) |
flags.DEFINE_integer( |
"max_num_train_instance_perquery", 1000, |
"The maximum number of training instances utilized from initial ranking" |
) |
flags.DEFINE_integer( |
"rerank_threshold", 100, |
"the maximum number of top documents to be reranked" |
) |
flags.DEFINE_string( |
"data_dir", None, |
"The input data dir. Should contain the .tsv files (or other data files) " |
"for the task.") |
flags.DEFINE_string( |
"bert_config_filename", None, |
"The config json file corresponding to the pre-trained BERT model. " |
"This specifies the model architecture.") |
flags.DEFINE_string("vocab_filename", None, |
"The vocabulary file that the BERT model was trained on.") |
flags.DEFINE_bool( |
"do_lower_case", True, |
"Whether to lower case the input text. Should be True for uncased " |
"models and False for cased models.") |
flags.DEFINE_string( |
"output_dir", None, |
"The output directory where the model checkpoints will be written.") |
flags.DEFINE_integer( |
"max_seq_length", 128, |
"The maximum total input sequence length after WordPiece tokenization. " |
"Sequences longer than this will be truncated, and sequences shorter " |
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