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70 lines
2.0 KiB
70 lines
2.0 KiB
# Copyright (c) 2017-present, Facebook, Inc.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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#
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"""
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Clone repo here: https://github.com/facebookresearch/SentEval.git
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"""
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from __future__ import absolute_import, division, unicode_literals
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import sys
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import logging
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import numpy as np
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from towhee import ops
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from statistics import mean
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import os
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import warnings
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from transformers import logging as t_logging
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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warnings.filterwarnings("ignore")
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t_logging.set_verbosity_error()
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model_name = sys.argv[-1]
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op = ops.sentence_embedding.transformers(model_name=model_name, device='cuda:3').get_op()
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# op = ops.text_embedding.sentence_transformers(model_name=model_name, device='cuda:3').get_op()
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# Set PATHs
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PATH_TO_SENTEVAL = '../'
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PATH_TO_DATA = '../data'
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# import SentEval
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sys.path.insert(0, PATH_TO_SENTEVAL)
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import senteval
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# SentEval prepare and batcher
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def prepare(params, samples):
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return
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def batcher(params, batch):
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batch = [' '.join(sent) if sent != [] else '.' for sent in batch]
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embeddings = op(batch)
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return np.vstack(embeddings)
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# Set params for SentEval
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params_senteval = {'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 10}
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params_senteval['classifier'] = {'nhid': 0, 'optim': 'adam', 'batch_size': 64,
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'tenacity': 5, 'epoch_size': 4}
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# Set up logger
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logging.basicConfig(format='%(asctime)s : %(message)s', level=logging.DEBUG)
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if __name__ == "__main__":
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se = senteval.engine.SE(params_senteval, batcher, prepare)
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# transfer_tasks = ['STSBenchmark']
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# transfer_tasks = ['STS16']
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transfer_tasks = ['STS12', 'STS13', 'STS14', 'STS15', 'STS16']
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results = se.eval(transfer_tasks)
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p = []
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s = []
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for t in transfer_tasks:
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res = results[t]['all']
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p.append(res['pearson']['mean'])
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s.append(res['spearman']['mean'])
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print('pearson:', mean(p))
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print('spearman:', mean(s))
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