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voorhs
requested changes
May 17, 2025
Samoed
reviewed
May 17, 2025
| verbose: bool = False, | ||
| **catboost_kwargs: Any, # noqa: ANN401 | ||
| ) -> None: | ||
| self.classification_model_config = EmbedderConfig.from_search_config(classification_model_config) |
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Если classification_model_config None, то создастся EmbedderConfig с дефолтными значениями. У тебя тест test_catboost_without_embedder получается не то тестирует
Samoed
reviewed
May 17, 2025
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| def encode(texts: list[str]) -> npt.NDArray[np.float32]: | ||
| with torch.no_grad(): | ||
| batch = tokenizer( |
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У EmbedderConfig есть параметры для токенизации в tokenizer_config
Samoed
requested changes
May 17, 2025
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| def _init_text_tools(self) -> None: | ||
| if not hasattr(self, "_tokenizer"): | ||
| self._tokenizer = Tokenizer(lowercasing=True, separator_type="BySense", token_types=["Word", "Number"]) | ||
| if not hasattr(self, "_dictionary"): | ||
| self._dictionary = Dictionary(occurence_lower_bound=1, gram_order=1) |
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| y_mat = np.zeros((len(labels), self._n_classes), dtype=np.float32) | ||
| for i, lbls in enumerate(cast("Sequence[Sequence[int]]", labels)): | ||
| for class_i, lbl in enumerate(lbls): | ||
| y_mat[i, class_i] = lbl | ||
| y = y_mat |
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Можно просто заменить на np.asarray(labels)?
Samoed
reviewed
May 21, 2025
| self._dictionary_fitted = False | ||
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| def get_embedder_config(self) -> dict[str, Any]: | ||
| return self.embedder_config.model_dump() |
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Если self._use_embedder False, то ошибка будет
Samoed
reviewed
May 21, 2025
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| if not hasattr(self, "_tokenizer"): | ||
| self._tokenizer = Tokenizer(lowercasing=True, separator_type="BySense", token_types=["Word", "Number"]) | ||
| if not hasattr(self, "_dictionary"): | ||
| self._dictionary = Dictionary(occurence_lower_bound=1, gram_order=1) | ||
| if not hasattr(self, "_dictionary_fitted"): | ||
| self._dictionary_fitted = False |
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Надо сделать как-то задаваемым как так? https://catboost.ai/docs/en/references/text-processing__test-processing__default-value
Просто кажется что можно не указывать tokenizer, dictironary и тд
* fix * sklearn scorer proper name * fix typing errors * try to fix pydantic errors
* Update wandb.py * Update wandb.py * Update wandb.py * Update _optimization_info.py * remove print
* fix few shot split * lint
* change how `clear_cache` is called * first version of early stopping * change mypy version * train_test_split bug fix * add `compute_metrics` and `EarlyStoppingCallback` * bug fix * fix mypy * try to fix `"eval_f1" not found` error * forgot to upd `from_context` * try to fix mypy * ty to fix "not found f1" error * refactor a little bit * disable early stopping for lora * fix typing errors * update contributing and makefile * minor change * use our metrics * add docstrings * set 3.10 for mypy * upd contributing.md * try to fix bug * try to fix typing issue * try to fix * add early stopping to ptuning
* add test for configuration * lint * satisfy mypy
* add prompt logging * Update optimizer_config.schema.json * fix --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
* fix default prompt * allow to use default prompt with override
Samoed
approved these changes
Jun 14, 2025
# Conflicts: # autointent/modules/scoring/_catboost/catboost_scorer.py # tests/modules/scoring/test_catboost.py
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В
catboost_scorer.pyдобавилdumpиloadдля CatBoostScorer. После можно будет их отредактировать и взять в_dump_tools