摘要：该论文提出了一种基于预训练 BERT 的新神经网络架构，称为 M-SQL。基于列的值提取分为值提取和值列匹配两个模块。
本文分享自华为云社区《基于ModelArts实现Text2SQL》，作者：HWCloudAI。
M-SQL: Multi-Task Representation Learning for Single-Table Text2sql Generation
虽然之前对 Text2SQL 的研究提供了一些可行的解决方案，但大多数都是基于列表示提取值。如果查询中有多个值，并且这些值属于不同的列，则以前基于列表示的方法无法准确提取值。该论文提出了一种基于预训练 BERT 的新神经网络架构，称为 M-SQL。基于列的值提取分为值提取和值列匹配两个模块。
论文地址：https://ieeexplore.ieee.org/document/9020099
注意事项：
1.本案例使用框架:PyTorch1.4.0
2.本案例使用硬件:GPU: 1*NVIDIA-V100NV32(32GB) | CPU: 8 核 64GB
3.运行代码方法: 点击本页面顶部菜单栏的三角形运行按钮或按Ctrl+Enter键 运行每个方块中的代码
4.JupyterLab的详细用法: 请参考《ModelAtrs JupyterLab使用指导》5.碰到问题的解决办法: 请参考《ModelAtrs JupyterLab常见问题解决办法》
1.下载代码和数据集
运行下面代码，进行数据和代码的下载和解压缩
使用TableQA数据集，数据位于m-sql/TableQA/中
import os
# 数据代码下载
!wget https://obs-aigallery-zc.obs.cn-north-4.myhuaweicloud.com/algorithm/m-sql.zip
# 解压缩
os.system('unzip m-sql.zip -d ./')
os.chdir('./m-sql')
2.训练
2.1安装依赖库
!pip install -r pip-requirements.txt
2.2训练所需参数和函数
import os
import argparse
import shutil
import sqlite3
import time
import tqdm
import torch
import random as python_random
from transformers import BertTokenizer, BertModel
import logging
import numpy as np
from model import Loss_sw_se, Seq2SQL_v1
# import moxing as mox
from sql_utils.utils_tableqa import load_tableqa, get_loader, get_fields, get_g, get_g_wvi, get_wemb_bert, \
pred_sw_se, convert_pr_wvi_to_string, generate_sql_i, extract_val, normalize_sql, get_acc, get_acc_x, \
save_for_evaluation, load_tableqa_data
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def construct_hyper_param(parser):
parser.add_argument("--eval", default='False', type=str)
parser.add_argument("--no_save", default='False', type=str)
parser.add_argument("--toy_model", default='False', type=str)
parser.add_argument("--toy_size", default=16, type=int)
parser.add_argument('--tepoch', default=1, type=int)
parser.add_argument('--print_per_step', default=50, type=int)
parser.add_argument("--bS", default=32, type=int,
help="Batch size")
parser.add_argument("--accumulate_gradients", default=1, type=int,
help="The number of accumulation of backpropagation to effectivly increase the batch size.")
parser.add_argument('--fine_tune',
default='False', type=str,
help="If present, BERT is trained.")
parser.add_argument("--data_url", default='./TableQA', type=str,
help="Saving path of model file, logfile and result file.")
parser.add_argument("--train_url", default='./data_and_model/', type=str,
help="Saving path of model file, logfile and result file.")
parser.add_argument("--vocab_file",
default='vocab.txt', type=str,
help="The vocabulary file that the BERT model was trained on.")
parser.add_argument("--max_seq_length",
default=512, type=int,
help="The maximum total input sequence length after WordPiece tokenization. Sequences ")
parser.add_argument("--num_target_layers",
default=1, type=int,
help="The Number of final layers of BERT to be used in downstream task.")
parser.add_argument('--lr_bert', default=1e-5, type=float, help='BERT model learning rate.')
parser.add_argument('--seed',
type=int,
default=1,
help="random seed for initialization")
parser.add_argument('--do_lower_case', default='False', type=str, help='whether to use lower case.')
parser.add_argument("--bert_url", default='./pre-trained_weights/chinese_wwm_ext_pytorch/', type=str,
help="Path or model name of BERT")
parser.add_argument("--load_weight", default='./trained_model/model/best_model.pth', type=str,
help="model path to load")
parser.add_argument('--dr', default=0, type=float, help="Dropout rate.")
parser.add_argument('--lr', default=1e-3, type=float, help="Learning rate.")
parser.add_argument('--num_warmup_steps', default=-1, type=int, help="num_warmup_steps")
parser.add_argument("--split", default='val', type=str, help='prefix of jsonl and db files')
args, _ = parser.parse_known_args()
python_random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
args.do_lower_case = args.do_lower_case == 'True'
args.fine_tune = args.fine_tune == 'True'
args.no_save = args.no_save == 'True'
args.eval = args.eval == 'True'
args.toy_model = args.toy_model == 'True'
return args
def get_bert(bert_path):
tokenizer = BertTokenizer.from_pretrained(bert_path)
model_bert = BertModel.from_pretrained(bert_path)
bert_config = model_bert.config
model_bert.to(device)
return model_bert, tokenizer, bert_config
def update_lr(param_groups, current_step, num_warmup_steps, start_lr):
if current_step <= num_warmup_steps:
warmup_frac_done = current_step / num_warmup_steps
new_lr = start_lr * warmup_frac_done
for param_group in param_groups:
param_group['lr'] = new_lr
def get_opt(model, model_bert, fine_tune):
if fine_tune:
opt = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr, weight_decay=0)
opt_bert = torch.optim.Adam(filter(lambda p: p.requires_grad, model_bert.parameters()),
lr=args.lr_bert, weight_decay=0)
else:
opt = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr, weight_decay=0)
opt_bert = None
return opt, opt_bert
def get_models(args, logger, bert_model, trained=False, path_model=None, eval=False):
# some constants
if not eval:
logger.info(f"Batch_size = {args.bS * args.accumulate_gradients}")
logger.info(f"BERT parameters:")
logger.info(f"learning rate: {args.lr_bert}")
logger.info(f"Fine-tune BERT: {args.fine_tune}")
# Get BERT
model_bert, tokenizer, bert_config = get_bert(bert_model)
iS = bert_config.hidden_size * args.num_target_layers
logger.info(bert_config.to_json_string())
# Get Seq-to-SQL
if not eval:
logger.info(f"Seq-to-SQL: the number of final BERT layers to be used: {args.num_target_layers}")
logger.info(f"Seq-to-SQL: learning rate = {args.lr}")
model = Seq2SQL_v1(iS, args.dr)
model = model.to(device)
if trained:
assert path_model != None
if torch.cuda.is_available():
res = torch.load(path_model)
else:
res = torch.load(path_model, map_location='cpu')
model_bert.load_state_dict(res['model_bert'])
model_bert.to(device)
model.load_state_dict(res['model'])
model.to(device)
return model, model_bert, tokenizer, bert_config
def get_data(path_wikisql, args):
train_data, train_table, dev_data, dev_table = load_tableqa(path_wikisql, args.toy_model, args.toy_size,
no_hs_tok=True)
train_loader, dev_loader = get_loader(train_data, dev_data, args.bS, shuffle_train=True)
return train_data, train_table, dev_data, dev_table, train_loader, dev_loader
def train(train_loader, train_table, model, model_bert, opt, bert_config, tokenizer,
max_seq_length, num_target_layers, accumulate_gradients, print_per_step, logger,
current_step, st_pos=0, opt_bert=None):
model.train()
model_bert.train()
torch.autograd.set_detect_anomaly(True)
ave_loss = 0
cnt = 0
for iB, t in enumerate(train_loader):
cnt += len(t)
if cnt < st_pos:
continue
# Get fields
nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hds = get_fields(t, train_table, no_hs_t=True, no_sql_t=True)
# nlu : natural language utterance
# nlu_t: tokenized nlu
# sql_i: canonical form of SQL query
# sql_q: full SQL query text. Not used.
# sql_t: tokenized SQL query
# tb : table
# hs_t : tokenized headers. Not used.
g_sn, g_sc, g_sa, g_wnop, g_wc, g_wo, g_wv = get_g(sql_i)
g_wvi, g_tags, g_value_match = get_g_wvi(t, g_wc)
wemb_cls, wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(bert_config, model_bert, tokenizer, nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
l_n_t = []
for t in t_to_tt_idx:
l_n_t.append(len(t))
# wemb_n: natural language embedding
# wemb_h: header embedding
# l_n: token lengths of each question
# l_hpu: header token lengths
# l_hs: the number of columns (headers) of the tables.
# score
s_sn, s_sc, s_sa, s_wnop, s_wc, \
s_wo, s_tags, s_match = model(wemb_cls, wemb_n, l_n_t, wemb_h, l_hpu, l_hs,
t_to_tt_idx=t_to_tt_idx,
g_sn=g_sn, g_sc=g_sc, g_sa=g_sa, g_wo=g_wo,
g_wnop=g_wnop, g_wc=g_wc, g_wvi=g_wvi,
g_tags=g_tags, g_vm=g_value_match)
# Calculate loss & step
loss = Loss_sw_se(s_sn, s_sc, s_sa, s_wnop, s_wc, s_wo, s_tags, s_match,
g_sn, g_sc, g_sa, g_wnop, g_wc, g_wo, g_tags, g_value_match)
if iB % accumulate_gradients == 0:
opt.zero_grad()
if opt_bert:
opt_bert.zero_grad()
loss.backward()
if accumulate_gradients == 1:
update_lr(opt.param_groups, current_step, args.num_warmup_steps, args.lr)
opt.step()
if opt_bert:
update_lr(opt_bert.param_groups, current_step, args.num_warmup_steps, args.lr_bert)
opt_bert.step()
current_step += 1
elif iB % accumulate_gradients == (accumulate_gradients - 1):
loss.backward()
update_lr(opt.param_groups, current_step, args.num_warmup_steps, args.lr)
opt.step()
if opt_bert:
update_lr(opt_bert.param_groups, current_step, args.num_warmup_steps, args.lr_bert)
opt_bert.step()
current_step += 1
else:
loss.backward()
# statistics
ave_loss += loss.item()
if iB % print_per_step == 0:
log = f'[Train Batch {iB}] '
logs = []
logs.append(f'average loss: {"%.4f" % (ave_loss / cnt,)}')
logger.info(log + ', '.join(logs))
if iB == 150:
logger.info('暂停训练，如需完整训练删除这个IF分支即可')
break
ave_loss /= cnt
return ave_loss, current_step
def test(data_loader, data_table, model, model_bert, bert_config, tokenizer, max_seq_length,
num_target_layers, print_per_step, logger, path_db, st_pos=0):
model.eval（)
model_bert.eval（)
cnt = 0
cnt_sn = 0
cnt_sc = 0
cnt_sa = 0
cnt_wnop = 0
cnt_wc = 0
cnt_wo = 0
cnt_wv = 0
cnt_lx = 0
cnt_x = 0
db_conn = sqlite3.connect(path_db)
cursor = db_conn.cursor()
results = []
for iB, t in enumerate(data_loader):
cnt += len(t)
if cnt < st_pos:
continue
# Get fields
nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hds = get_fields(t, data_table, no_hs_t=True, no_sql_t=True)
wemb_cls, wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(bert_config, model_bert, tokenizer, nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
l_n_t = []
for t in t_to_tt_idx:
l_n_t.append(len(t))
# score
s_sn, s_sc, s_sa, s_wnop, s_wc, \
s_wo, s_tags, s_match = model(wemb_cls, wemb_n, l_n_t, wemb_h, l_hpu, l_hs, t_to_tt_idx)
# prediction
pr_sn, pr_sc, pr_sa, pr_wn, pr_conn_op, \
pr_wc, pr_wo, pr_tags, pr_wvi = pred_sw_se(s_sn, s_sc, s_sa, s_wnop, s_wc, s_wo, s_tags, s_match, l_n_t)
pr_wv_str = convert_pr_wvi_to_string(pr_wvi, nlu_t)
pr_sql_i = generate_sql_i(pr_sc, pr_sa, pr_conn_op, pr_wc, pr_wo, pr_wv_str, nlu)
value_indexes, value_nums = extract_val(pr_tags, l_n_t)
# Saving for the official evaluation later.
for b, pr_sql_i1 in enumerate(pr_sql_i):
normalize_sql(pr_sql_i1, tb[b])
results1 = {}
results1["sql"] = pr_sql_i1
results1["gold_sql"] = sql_i[b]
results1["table_id"] = tb[b]["id"]
results1["nlu"] = nlu[b]
results1['value_indexes'] = value_indexes[b]
results1['value_nums'] = value_nums[b]
results1['pr_wc'] = pr_wc[b]
sn, sc, sa, co, wn, wc, wo, wv, cond, sql = \
get_acc(sql_i[b], pr_sql_i1, pr_wc[b], pr_wo[b], tb[b], normalized=True)
cnt_sn += sn
cnt_sc += sc
cnt_sa += sa
cnt_wnop += (co and wn)
cnt_wc += wc
cnt_wo += wo
cnt_wv += wv
cnt_lx += sql
results1['correct'] = sql
execution, res = get_acc_x(sql_i[b], pr_sql_i1, tb[b], cursor)
cnt_x += execution
results1['ex_correct'] = execution
results1['result'] = res
results.append(results1)
# print acc
cnts = [cnt_sn, cnt_sc, cnt_sa, cnt_wnop, cnt_wc,
cnt_wo, cnt_wv, cnt_lx, cnt_x, (cnt_lx + cnt_x) / 2]
cnt_desc = [
's-num', 's-col', 's-col-agg', 'w-num-op', 'w-col',
'w-col-op', 'w-col-value', 'acc_lx', 'acc_x', 'acc_mx'
]
if iB % print_per_step == 0:
log = f'[Test Batch {iB}] '
logs = []
for k, metric in enumerate(cnts):
logs.append(cnt_desc[k] + ': ' + '%.4f' % (metric / cnt,))
logger.info(log + ', '.join(logs))
if iB == 150:
logger.info('暂停训练，如需完整训练删除这个IF分支即可')
break
acc_sn = cnt_sn / cnt
acc_sc = cnt_sc / cnt
acc_sa = cnt_sa / cnt
acc_wnop = cnt_wnop / cnt
acc_wc = cnt_wc / cnt
acc_wo = cnt_wo / cnt
acc_wv = cnt_wv / cnt
acc_lx = cnt_lx / cnt
acc_x = cnt_x / cnt
acc_mx = (acc_lx + acc_x) / 2
acc = [acc_sn, acc_sc, acc_sa, acc_wnop, acc_wc,
acc_wo, acc_wv, acc_lx, acc_x, acc_mx]
return acc, results, acc_lx
def print_result(epoch, acc, dname, logger=None):
if logger:
logger.info(f'------------ {dname} results ------------')
if dname == 'dev':
acc_sn, acc_sc, acc_sa, acc_wnop, acc_wc, \
acc_wo, acc_wv, acc_lx, acc_x, acc_mx = acc
logger.info(
f" Epoch: {epoch}, s-num: {acc_sn:.4f}, s-col: {acc_sc:.4f},"
f" s-col-agg: {acc_sa:.4f}, w-num-op: {acc_wnop:.4f},"
f" w-col: {acc_wc:.4f}, w-col-op: {acc_wo:.4f}, w-col-value: {acc_wv:.4f},"
f" acc_lx: {acc_lx:.4f}, acc_x: {acc_x:.4f}, acc_mx: {acc_mx:.4f}"
)
else:
logger.info(f" Epoch: {epoch}, average loss: {acc}")
def get_logger(log_fp=None):
logging.basicConfig(level=logging.INFO,
format='[%(asctime)s] %(message)s')
logger = logging.getLogger(__name__)
if log_fp:
handler = logging.FileHandler(log_fp)
handler.setLevel(logging.INFO)
formatter = logging.Formatter('[%(asctime)s] %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
return logger
def predict(data_loader, data_table, model, model_bert, bert_config, tokenizer,
max_seq_length, num_target_layers, path_db):
model.eval（)
model_bert.eval（)
results = []
cnt = 0
cnt_sn = 0
cnt_sc = 0
cnt_sa = 0
cnt_wnop = 0
cnt_wc = 0
cnt_wo = 0
cnt_wv = 0
cnt_lx = 0
cnt_x = 0
db_conn = sqlite3.connect(path_db)
cursor = db_conn.cursor()
for iB, t in tqdm.tqdm(enumerate(data_loader)):
nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hds = get_fields(t, data_table, no_hs_t=True, no_sql_t=True)
wemb_cls, wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(bert_config, model_bert, tokenizer, nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
l_n_t = []
for t in t_to_tt_idx:
l_n_t.append(len(t))
s_sn, s_sc, s_sa, s_wnop, s_wc, \
s_wo, s_tags, s_match = model(wemb_cls, wemb_n, l_n_t, wemb_h, l_hpu, l_hs, t_to_tt_idx)
# prediction
pr_sn, pr_sc, pr_sa, pr_wn, pr_conn_op, \
pr_wc, pr_wo, pr_tags, pr_wvi = pred_sw_se(s_sn, s_sc, s_sa, s_wnop, s_wc, s_wo, s_tags, s_match, l_n_t)
pr_wv_str = convert_pr_wvi_to_string(pr_wvi, nlu_t)
pr_sql_i = generate_sql_i(pr_sc, pr_sa, pr_conn_op, pr_wc, pr_wo, pr_wv_str, nlu)
value_indexes, value_nums = extract_val(pr_tags, l_n_t)
for b, pr_sql_i1 in enumerate(pr_sql_i):
cnt += 1
results1 = {}
normalize_sql(pr_sql_i1, tb[b])
results1["table_id"] = tb[b]["id"]
results1["nlu"] = nlu[b]
results1["sql"] = pr_sql_i1
if sql_i[b]:
results1["gold_sql"] = sql_i[b]
results1['value_indexes'] = value_indexes[b]
results1['value_nums'] = value_nums[b]
results1['pr_wc'] = pr_wc[b]
if sql_i[b]:
sn, sc, sa, co, wn, wc, wo, wv, cond, sql =\
get_acc(sql_i[b], pr_sql_i1, pr_wc[b], pr_wo[b], tb[b], normalized=True)
cnt_sn += sn
cnt_sc += sc
cnt_sa += sa
cnt_wnop += (wn and co)
cnt_wc += wc
cnt_wo += wo
cnt_wv += wv
cnt_lx += sql
results1['correct'] = sql
execution, res = get_acc_x(sql_i[b], pr_sql_i1, tb[b], cursor)
cnt_x += execution
results1['ex_correct'] = execution
results1['result'] = res
results.append(results1)
cnts = [cnt_sn, cnt_sc, cnt_sa, cnt_wnop, cnt_wc,
cnt_wo, cnt_wv, cnt_lx, cnt_x, (cnt_x + cnt_lx) / 2]
if sum(cnts) > 0:
cnt_desc = [
's-num', 's-col', 's-col-agg', 'w-num-op', 'w-col',
'w-col-op', 'w-col-value', 'acc_lx', 'acc_x', 'acc_mx'
]
logger.info('--------- eval result ---------')
for k, metric in enumerate(cnts):
logger.info(cnt_desc[k] + ': ' + '%.4f' % (metric / cnt,))
else:
cnts = None
cnt_desc = None
return results, cnt, cnts, cnt_desc
2.3开始训练
if __name__ == '__main__':
# Hyper parameters
parser = argparse.ArgumentParser()
args = construct_hyper_param(parser)
save_path = args.train_url
if not os.path.exists(save_path):
os.makedirs(save_path)
if not args.eval:
_model_path = './trained_model/model/'
shutil.copytree(_model_path, os.path.join(save_path, 'model'))
t = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
log_fp = os.path.join(save_path, f'{t}.log')
logger = get_logger(log_fp)
logger.info(f"BERT-Model: {args.bert_url}")
trained = args.load_weight is not None and args.load_weight != 'None'
load_path = None
if trained:
load_path = '/home/work/modelarts/inputs/best_model.pt'
if args.load_weight and args.load_weight.startswith('obs://'):
if not os.path.exists(load_path):
mox.file.copy_parallel(args.load_weight, load_path)
print('copy %s to %s' % (args.load_weight, load_path))
else:
print(load_path, 'already exists')
else:
load_path = args.load_weight
train_input_dir = args.data_url
bert_model = args.bert_url
# Paths
path_wikisql = train_input_dir
path_val_db = os.path.join(train_input_dir, 'val.db')
path_save_for_evaluation = save_path
# Build & Load models
if args.eval and not trained:
print('in eval mode, "--load_weight" must be provided!')
exit(-1)
if not trained:
model, model_bert, tokenizer, bert_config = get_models(args, logger, bert_model, eval=args.eval)
else:
path_model = load_path
model, model_bert, tokenizer, bert_config = get_models(args, logger, bert_model,
trained=True, path_model=path_model,
eval=args.eval)
if not args.eval:
train_data, train_table, dev_data, dev_table, train_loader, dev_loader = get_data(path_wikisql, args)
opt, opt_bert = get_opt(model, model_bert, args.fine_tune)
acc_lx_t_best = -1
epoch_best = -1
current_step = 1
for epoch in range(args.tepoch):
# train
logger.info(f'Training Epoch {epoch}')
ave_loss_train, current_step = train(train_loader,
train_table,
model,
model_bert,
opt,
bert_config,
tokenizer,
args.max_seq_length,
args.num_target_layers,
args.accumulate_gradients,
args.print_per_step,
logger=logger,
current_step=current_step,
opt_bert=opt_bert,
st_pos=0)
# check DEV
with torch.no_grad():
logger.info(f'Testing on dev Epoch {epoch}:')
acc_dev, results_dev, \
dev_acc_lx = test(dev_loader,
dev_table,
model,
model_bert,
bert_config,
tokenizer,
args.max_seq_length,
args.num_target_layers,
args.print_per_step,
logger=logger,
path_db=path_val_db,
st_pos=0)
print_result(epoch, ave_loss_train, 'train', logger=logger)
print_result(epoch, acc_dev, 'dev', logger=logger)
# save results for the official evaluation
path_save_file = save_for_evaluation(path_save_for_evaluation,
results_dev, 'dev', epoch=epoch)
# mox.file.copy_parallel(path_save_file,
# args.train_url + f'results_dev-{epoch}.jsonl')
# save best model
# Based on Dev Set logical accuracy lx
if dev_acc_lx > acc_lx_t_best:
acc_lx_t_best = dev_acc_lx
epoch_best = epoch
# save model
if not args.no_save:
state = {'model': model.state_dict(),
'model_bert': model_bert.state_dict()}
torch.save(state, os.path.join(save_path, 'model', f'best_model.pth'))
logger.info(f" Best Dev lx acc: {acc_lx_t_best} at epoch: {epoch_best}")
else:
try:
dev_data, dev_table = load_tableqa_data(path_wikisql, mode=args.split, no_hs_tok=True)
except Exception:
logger.error('未找到输入文件！')
exit(-1)
dev_loader = torch.utils.data.DataLoader(
batch_size=args.bS,
dataset=dev_data,
shuffle=False,
num_workers=1,
collate_fn=lambda x: x
)
with torch.no_grad():
results, cnt, cnts, cnt_desc \
= predict(dev_loader,
dev_table,
model,
model_bert,
bert_config,
tokenizer,
args.max_seq_length,
args.num_target_layers,
os.path.join(train_input_dir, args.split + '.db'))
save_for_evaluation(os.path.join(save_path, 'pred_results.jsonl'),
results, args.split, 'pred', use_filename=True)
if cnts:
with open(os.path.join(save_path, 'eval_result.txt'), 'w') as f_eval:
f_eval.write('--------- eval result ---------\n')
for k, metric in enumerate(cnts):
f_eval.write(cnt_desc[k] + ': ' + '%.4f' % (metric / cnt,) + '\n')
3.模型测试
from trained_model.model.customize_service import *
if __name__ == '__main__':
model_path = r'./outputs/model/best_model.pth'
my_model = ModelClass('', model_path)
data = {
"question": "近四周成交量小于3574套并且环比低于69.7%的城市有几个",
"table_id": "252c7b6b302e11e995ee542696d6e445"
}
data = my_model._preprocess(data)
result = my_model._inference(data)
print(json.dumps(dict(result), ensure_ascii=False, indent=2))