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evaluation.py
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evaluation.py
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import sys, logging, torch, hydra
from prettytable import PrettyTable
from transformers import AutoModel, AutoTokenizer, TrainingArguments, AutoConfig
import importlib
from types import SimpleNamespace
# Set up logger
logging.basicConfig(format='%(asctime)s : %(message)s', level=logging.DEBUG)
logger = logging.getLogger(__name__)
# Set PATHs
PATH_TO_SENTEVAL = './SentEval'
PATH_TO_DATA = './SentEval/data'
# Import SentEval
sys.path.insert(0, PATH_TO_SENTEVAL)
import senteval
def print_table(task_names, scores):
tb = PrettyTable()
tb.field_names = task_names
tb.add_row(scores)
logger.info("\n"+str(tb))
@hydra.main(config_path="configs", config_name="default_eval")
def main(cfg):
# parser = argparse.ArgumentParser()
# parser.add_argument("--model_name_or_path", type=str,
# help="Transformers' model name or path")
# parser.add_argument("--pooler", type=str,
# choices=['cls', 'cls_before_pooler', 'avg', 'avg_top2', 'avg_first_last'],
# default='cls',
# help="Which pooler to use")
# parser.add_argument("--mode", type=str,
# choices=['dev', 'test', 'fasttest'],
# default='test',
# help="What evaluation mode to use (dev: fast mode, dev results; test: full mode, test results); fasttest: fast mode, test results")
# parser.add_argument("--task_set", type=str,
# choices=['sts', 'transfer', 'full', 'na'],
# default='sts',
# help="What set of tasks to evaluate on. If not 'na', this will override '--tasks'")
# parser.add_argument("--tasks", type=str, nargs='+',
# default=['STS12', 'STS13', 'STS14', 'STS15', 'STS16',
# 'MR', 'CR', 'MPQA', 'SUBJ', 'SST2', 'TREC', 'MRPC',
# 'SICKRelatedness', 'STSBenchmark'],
# help="Tasks to evaluate on. If '--task_set' is specified, this will be overridden")
# args = parser.parse_args()
model_args = SimpleNamespace(**cfg.model_args)
data_args = SimpleNamespace(**cfg.data_args)
trainer_args = SimpleNamespace(**cfg.trainer_args)
eval_args = SimpleNamespace(**cfg.eval_args)
training_args = TrainingArguments(**cfg.training_args)
config = AutoConfig.from_pretrained(model_args.model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(training_args.output_dir)
# Load transformers' model checkpoint
model_class = getattr(importlib.import_module(f"..{model_args.model_class}", package="simcse.models.subpkg"), model_args.model_class)
model_class_args = {}
mask_token_id = tokenizer.mask_token_id
for k in model_args.model_class_args:
model_class_args[k] = eval(k)
model = model_class.from_pretrained(
training_args.output_dir,
**model_class_args,
)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()
# Set up the tasks
if eval_args.task_set == 'sts':
eval_args.tasks = ['STS12', 'STS13', 'STS14', 'STS15', 'STS16', 'STSBenchmark', 'SICKRelatedness']
elif eval_args.task_set == 'transfer':
eval_args.tasks = ['MR', 'CR', 'MPQA', 'SUBJ', 'SST2', 'TREC', 'MRPC']
elif eval_args.task_set == 'full':
eval_args.tasks = ['STS12', 'STS13', 'STS14', 'STS15', 'STS16', 'STSBenchmark', 'SICKRelatedness']
eval_args.tasks += ['MR', 'CR', 'MPQA', 'SUBJ', 'SST2', 'TREC', 'MRPC']
# Set params for SentEval
if eval_args.mode == 'dev' or eval_args.mode == 'fasttest':
# Fast mode
params = {'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 5}
params['classifier'] = {'nhid': 0, 'optim': 'rmsprop', 'batch_size': 128,
'tenacity': 3, 'epoch_size': 2}
elif eval_args.mode == 'test':
# Full mode
params = {'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 10}
params['classifier'] = {'nhid': 0, 'optim': 'adam', 'batch_size': 64,
'tenacity': 5, 'epoch_size': 4}
else:
raise NotImplementedError
# SentEval prepare and batcher
def prepare(params, samples):
return
batcher_function_args = {}
for k in trainer_args.batcher_function_args:
batcher_function_args[k] = eval(k)
get_batcher = getattr(importlib.import_module(f"..{trainer_args.batcher_function}", package = f"simcse.trainers.batcher_functions.subpkg"),trainer_args.batcher_function)
batcher_function = get_batcher(**batcher_function_args)
results = {}
for task in eval_args.tasks:
se = senteval.engine.SE(params, batcher_function, prepare)
result = se.eval(task)
results[task] = result
# Print evaluation results
if eval_args.mode == 'dev':
logger.info("------ %s ------" % (eval_args.mode))
task_names = []
scores = []
for task in ['STSBenchmark', 'SICKRelatedness']:
task_names.append(task)
if task in results:
scores.append("%.2f" % (results[task]['dev']['spearman'][0] * 100))
else:
scores.append("0.00")
print_table(task_names, scores)
task_names = []
scores = []
for task in ['MR', 'CR', 'SUBJ', 'MPQA', 'SST2', 'TREC', 'MRPC']:
task_names.append(task)
if task in results:
scores.append("%.2f" % (results[task]['devacc']))
else:
scores.append("0.00")
task_names.append("Avg.")
scores.append("%.2f" % (sum([float(score) for score in scores]) / len(scores)))
print_table(task_names, scores)
elif eval_args.mode == 'test' or eval_args.mode == 'fasttest':
logger.info("------ %s ------" % (eval_args.mode))
task_names = []
scores = []
for task in ['STS12', 'STS13', 'STS14', 'STS15', 'STS16', 'STSBenchmark', 'SICKRelatedness']:
task_names.append(task)
if task in results:
if task in ['STS12', 'STS13', 'STS14', 'STS15', 'STS16']:
scores.append("%.2f" % (results[task]['all']['spearman']['all'] * 100))
else:
scores.append("%.2f" % (results[task]['test']['spearman'].correlation * 100))
else:
scores.append("0.00")
task_names.append("Avg.")
scores.append("%.2f" % (sum([float(score) for score in scores]) / len(scores)))
print_table(task_names, scores)
task_names = []
scores = []
for task in ['MR', 'CR', 'SUBJ', 'MPQA', 'SST2', 'TREC', 'MRPC']:
task_names.append(task)
if task in results:
scores.append("%.2f" % (results[task]['acc']))
else:
scores.append("0.00")
task_names.append("Avg.")
scores.append("%.2f" % (sum([float(score) for score in scores]) / len(scores)))
print_table(task_names, scores)
if __name__ == "__main__":
main()