| # | |
| # 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 | |