Sourcery refactored master branch #3
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| if len(keyword_sentence_mapping.keys()) == 0: | ||
| return final_output | ||
| else: | ||
| if len(keyword_sentence_mapping.keys()) != 0: |
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Function QGen.predict_mcq refactored with the following changes:
- Hoist nested repeated code outside conditional statements (
hoist-similar-statement-from-if)
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| self.text = "paraphrase: " + self.sentence + " </s>" | ||
| self.text = f"paraphrase: {self.sentence} </s>" |
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Function QGen.paraphrase refactored with the following changes:
- Use f-string instead of string concatenation [×2] (
use-fstring-for-concatenation) - Merge dictionary assignment with declaration [×3] (
merge-dict-assign) - Replace call to format with f-string (
use-fstring-for-formatting)
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| form = "truefalse: %s passage: %s </s>" % (modified_text, answer) | ||
| form = f"truefalse: {modified_text} passage: {answer} </s>" |
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Function BoolQGen.predict_boolq refactored with the following changes:
- Replace interpolated string formatting with f-string (
replace-interpolation-with-fstring) - Merge dictionary assignment with declaration [×3] (
merge-dict-assign) - Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| def greedy_decoding(inp_ids, attn_mask, model, tokenizer): | ||
| greedy_output = model.generate(input_ids=inp_ids, attention_mask=attn_mask, max_length=256) | ||
| def greedy_decoding(self, attn_mask, model, tokenizer): | ||
| greedy_output = model.generate( | ||
| input_ids=self, attention_mask=attn_mask, max_length=256 | ||
| ) | ||
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Function AnswerPredictor.greedy_decoding refactored with the following changes:
- The first argument to instance methods should be
self(instance-method-first-arg-name)
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| input = "question: %s <s> context: %s </s>" % (question, context) | ||
| input = f"question: {question} <s> context: {context} </s>" | ||
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| encoding = self.tokenizer.encode_plus(input, return_tensors="pt") | ||
| input_ids, attention_masks = encoding["input_ids"].to(self.device), encoding["attention_mask"].to(self.device) | ||
| greedy_output = self.model.generate(input_ids=input_ids, attention_mask=attention_masks, max_length=256) | ||
| Question = self.tokenizer.decode(greedy_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=True) | ||
| output = Question.strip().capitalize() | ||
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| return output | ||
| return Question.strip().capitalize() |
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Function AnswerPredictor.predict_answer refactored with the following changes:
- Replace interpolated string formatting with f-string (
replace-interpolation-with-fstring) - Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], | ||
| "params": [ | ||
| p | ||
| for n, p in model.named_parameters() | ||
| if all(nd not in n for nd in no_decay) | ||
| ], | ||
| "weight_decay": self.hparams.weight_decay, | ||
| }, | ||
| { | ||
| "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], | ||
| "params": [ | ||
| p | ||
| for n, p in model.named_parameters() | ||
| if any(nd in n for nd in no_decay) | ||
| ], | ||
| "weight_decay": 0.0, | ||
| }, | ||
| ] | ||
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Function T5FineTuner.configure_optimizers refactored with the following changes:
- Invert any/all to simplify comparisons (
invert-any-all)
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| tqdm_dict = {"loss": "{:.3f}".format(self.trainer.avg_loss), "lr": self.lr_scheduler.get_last_lr()[-1]} | ||
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| return tqdm_dict | ||
| return { | ||
| "loss": "{:.3f}".format(self.trainer.avg_loss), | ||
| "lr": self.lr_scheduler.get_last_lr()[-1], | ||
| } |
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Function T5FineTuner.get_tqdm_dict refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| logger.info("{} = {}\n".format(key, str(metrics[key]))) | ||
| logger.info(f"{key} = {str(metrics[key])}\n") |
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Function LoggingCallback.on_validation_end refactored with the following changes:
- Replace call to format with f-string (
use-fstring-for-formatting)
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| logger.info("{} = {}\n".format(key, str(metrics[key]))) | ||
| writer.write("{} = {}\n".format(key, str(metrics[key]))) | ||
| logger.info(f"{key} = {str(metrics[key])}\n") | ||
| writer.write(f"{key} = {str(metrics[key])}\n") |
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Function LoggingCallback.on_test_end refactored with the following changes:
- Replace call to format with f-string [×2] (
use-fstring-for-formatting)
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| self.data.loc[idx, self.target_column] | ||
| self.data.loc[idx, self.target_column] | ||
| true_false = str(true_false) | ||
| if true_false.lower() == "true": | ||
| true_false = "yes" | ||
| else: | ||
| true_false = "no" | ||
| true_false = "yes" if true_false.lower() == "true" else "no" | ||
| # input_ = "paraphrase: "+ input_ + ' </s>' | ||
| input_ = "truefalse: %s passage: %s </s>" % (true_false, passage) | ||
| input_ = f"truefalse: {true_false} passage: {passage} </s>" |
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Function BooleanDataset._build refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp) - Replace interpolated string formatting with f-string (
replace-interpolation-with-fstring)
Sourcery Code Quality Report✅ Merging this PR will increase code quality in the affected files by 0.62%.
Here are some functions in these files that still need a tune-up:
Legend and ExplanationThe emojis denote the absolute quality of the code:
The 👍 and 👎 indicate whether the quality has improved or gotten worse with this pull request. Please see our documentation here for details on how these metrics are calculated. We are actively working on this report - lots more documentation and extra metrics to come! Help us improve this quality report! |
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masterrefactored by Sourcery.If you're happy with these changes, merge this Pull Request using the Squash and merge strategy.
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