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Runtime error
Runtime error
Gagan Bhatia
commited on
Commit
·
fafca07
1
Parent(s):
056c147
Update model.py
Browse files- src/models/model.py +248 -0
src/models/model.py
CHANGED
@@ -185,3 +185,251 @@ class LightningModel(LightningModule):
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input_ids=input_ids,
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attention_mask=attention_mask,
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decoder_attention_mask=labels_attention_mask,
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input_ids=input_ids,
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attention_mask=attention_mask,
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decoder_attention_mask=labels_attention_mask,
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labels=labels,
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)
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self.log("train_loss", loss, prog_bar=True, logger=True)
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return loss
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+
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+
def validation_step(self, batch, batch_size):
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""" validation step """
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input_ids = batch["keywords_input_ids"]
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attention_mask = batch["keywords_attention_mask"]
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labels = batch["labels"]
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labels_attention_mask = batch["labels_attention_mask"]
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loss, outputs = self(
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input_ids=input_ids,
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attention_mask=attention_mask,
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decoder_attention_mask=labels_attention_mask,
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labels=labels,
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)
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self.log("val_loss", loss, prog_bar=True, logger=True)
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return loss
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def test_step(self, batch, batch_size):
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""" test step """
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input_ids = batch["keywords_input_ids"]
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attention_mask = batch["keywords_attention_mask"]
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labels = batch["labels"]
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labels_attention_mask = batch["labels_attention_mask"]
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loss, outputs = self(
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input_ids=input_ids,
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attention_mask=attention_mask,
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decoder_attention_mask=labels_attention_mask,
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labels=labels,
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)
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self.log("test_loss", loss, prog_bar=True, logger=True)
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return loss
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def configure_optimizers(self):
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""" configure optimizers """
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model = self.model
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no_decay = ["bias", "LayerNorm.weight"]
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optimizer_grouped_parameters = [
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{
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"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
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"weight_decay": self.hparams.weight_decay,
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},
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{
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"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
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"weight_decay": 0.0,
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},
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]
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optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)
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self.opt = optimizer
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return [optimizer]
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class Summarization:
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""" Custom Summarization class """
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def __init__(self) -> None:
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""" initiates Summarization class """
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pass
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def from_pretrained(self, model_name="t5-base") -> None:
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"""
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loads T5/MT5 Model model for training/finetuning
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Args:
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model_name (str, optional): exact model architecture name, "t5-base" or "t5-large". Defaults to "t5-base".
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"""
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self.tokenizer = T5Tokenizer.from_pretrained(f"{model_name}")
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self.model = T5ForConditionalGeneration.from_pretrained(
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f"{model_name}", return_dict=True
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)
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def train(
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self,
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train_df: pd.DataFrame,
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eval_df: pd.DataFrame,
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source_max_token_len: int = 512,
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target_max_token_len: int = 512,
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batch_size: int = 8,
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max_epochs: int = 5,
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use_gpu: bool = True,
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outputdir: str = "models",
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early_stopping_patience_epochs: int = 0, # 0 to disable early stopping feature
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):
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"""
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trains T5/MT5 model on custom dataset
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Args:
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train_df (pd.DataFrame): training datarame. Dataframe must have 2 column --> "input_text" and "output_text"
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eval_df ([type], optional): validation datarame. Dataframe must have 2 column --> "input_text" and
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"output_text"
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source_max_token_len (int, optional): max token length of source text. Defaults to 512.
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target_max_token_len (int, optional): max token length of target text. Defaults to 512.
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batch_size (int, optional): batch size. Defaults to 8.
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max_epochs (int, optional): max number of epochs. Defaults to 5.
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use_gpu (bool, optional): if True, model uses gpu for training. Defaults to True.
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outputdir (str, optional): output directory to save model checkpoints. Defaults to "outputs".
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early_stopping_patience_epochs (int, optional): monitors val_loss on epoch end and stops training,
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if val_loss does not improve after the specied number of epochs. set 0 to disable early stopping.
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Defaults to 0 (disabled)
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"""
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self.target_max_token_len = target_max_token_len
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self.data_module = PLDataModule(
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train_df,
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eval_df,
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self.tokenizer,
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batch_size=batch_size,
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source_max_token_len=source_max_token_len,
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target_max_token_len=target_max_token_len,
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)
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self.T5Model = LightningModel(
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tokenizer=self.tokenizer, model=self.model, output=outputdir
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)
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# checkpoint_callback = ModelCheckpoint(
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# dirpath="checkpoints",
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# filename="best-checkpoint-{epoch}-{train_loss:.2f}",
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# save_top_k=-1,
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# verbose=True,
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# monitor="train_loss",
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# mode="min",
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# )
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logger = MLFlowLogger(experiment_name="Summarization")
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early_stop_callback = (
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[
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EarlyStopping(
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monitor="val_loss",
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min_delta=0.00,
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patience=early_stopping_patience_epochs,
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verbose=True,
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mode="min",
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)
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]
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if early_stopping_patience_epochs > 0
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else None
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)
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gpus = 1 if use_gpu else 0
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trainer = Trainer(
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logger=logger,
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callbacks=early_stop_callback,
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max_epochs=max_epochs,
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gpus=gpus,
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progress_bar_refresh_rate=5,
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)
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trainer.fit(self.T5Model, self.data_module)
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def load_model(
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self, model_dir: str = "../../models", use_gpu: bool = False
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):
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"""
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loads a checkpoint for inferencing/prediction
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Args:
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model_type (str, optional): "t5" or "mt5". Defaults to "t5".
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model_dir (str, optional): path to model directory. Defaults to "outputs".
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use_gpu (bool, optional): if True, model uses gpu for inferencing/prediction. Defaults to True.
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"""
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self.model = T5ForConditionalGeneration.from_pretrained(f"{model_dir}")
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self.tokenizer = T5Tokenizer.from_pretrained(f"{model_dir}")
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if use_gpu:
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if torch.cuda.is_available():
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self.device = torch.device("cuda")
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else:
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raise Exception("exception ---> no gpu found. set use_gpu=False, to use CPU")
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else:
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self.device = torch.device("cpu")
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self.model = self.model.to(self.device)
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def save_model(
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self,
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model_dir="../../models"
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):
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"""
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Save model to dir
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:param model_dir:
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:return: model is saved
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"""
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path = f"{model_dir}"
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self.tokenizer.save_pretrained(path)
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self.model.save_pretrained(path)
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def predict(
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self,
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source_text: str,
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max_length: int = 512,
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num_return_sequences: int = 1,
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num_beams: int = 2,
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top_k: int = 50,
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top_p: float = 0.95,
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do_sample: bool = True,
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repetition_penalty: float = 2.5,
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length_penalty: float = 1.0,
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early_stopping: bool = True,
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skip_special_tokens: bool = True,
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clean_up_tokenization_spaces: bool = True,
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):
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"""
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generates prediction for T5/MT5 model
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Args:
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source_text (str): any text for generating predictions
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max_length (int, optional): max token length of prediction. Defaults to 512.
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num_return_sequences (int, optional): number of predictions to be returned. Defaults to 1.
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num_beams (int, optional): number of beams. Defaults to 2.
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top_k (int, optional): Defaults to 50.
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top_p (float, optional): Defaults to 0.95.
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do_sample (bool, optional): Defaults to True.
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repetition_penalty (float, optional): Defaults to 2.5.
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length_penalty (float, optional): Defaults to 1.0.
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early_stopping (bool, optional): Defaults to True.
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skip_special_tokens (bool, optional): Defaults to True.
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clean_up_tokenization_spaces (bool, optional): Defaults to True.
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Returns:
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list[str]: returns predictions
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"""
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input_ids = self.tokenizer.encode(
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source_text, return_tensors="pt", add_special_tokens=True
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)
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input_ids = input_ids.to(self.device)
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generated_ids = self.model.generate(
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input_ids=input_ids,
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num_beams=num_beams,
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max_length=max_length,
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repetition_penalty=repetition_penalty,
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length_penalty=length_penalty,
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early_stopping=early_stopping,
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top_p=top_p,
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top_k=top_k,
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num_return_sequences=num_return_sequences,
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)
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preds = [
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self.tokenizer.decode(
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g,
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skip_special_tokens=skip_special_tokens,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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)
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for g in generated_ids
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]
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return preds
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