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# | |
# Pyserini: Reproducible IR research with sparse and dense representations | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
# | |
from transformers import AutoTokenizer | |
from pyserini.encode import QueryEncoder | |
class TokFreqQueryEncoder(QueryEncoder): | |
def __init__(self, model_name_or_path=None): | |
self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) if model_name_or_path else None | |
def encode(self, text, **kwargs): | |
vector = {} | |
if self.tokenizer is not None: | |
tok_list = self.tokenizer.tokenize(text) | |
else: | |
tok_list = text.strip().split() | |
for tok in tok_list: | |
if tok not in vector: | |
vector[tok] = 1 | |
else: | |
vector[tok] += 1 | |
return vector | |