update docstrings; rename lm_labels to more explicit ltr_lm_labels

examples/run_summarization_finetuning.py

@@ -26,7 +26,7 @@ import numpy as np
 from tqdm import tqdm, trange
 import torch
 from torch.optim import Adam
-from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampler
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 
 from transformers import (
     AutoTokenizer,
@@ -283,14 +283,14 @@ def evaluate(args, model, tokenizer, prefix=""):
     model.eval()
 
     for batch in tqdm(eval_dataloader, desc="Evaluating"):
-        source, target, encoder_token_type_ids, encoder_mask, decoder_mask, lm_labels = batch
+        source, target, encoder_token_type_ids, encoder_mask, decoder_mask, ltr_lm_labels = batch
 
         source = source.to(args.device)
         target = target.to(args.device)
         encoder_token_type_ids = encoder_token_type_ids.to(args.device)
         encoder_mask = encoder_mask.to(args.device)
         decoder_mask = decoder_mask.to(args.device)
-        lm_labels = lm_labels.to(args.device)
+        ltr_lm_labels = ltr_lm_labels.to(args.device)
 
         with torch.no_grad():
             outputs = model(
@@ -299,7 +299,7 @@ def evaluate(args, model, tokenizer, prefix=""):
                 encoder_token_type_ids=encoder_token_type_ids,
                 encoder_attention_mask=encoder_mask,
                 decoder_attention_mask=decoder_mask,
-                decoder_lm_labels=lm_labels,
+                decoder_ltr_lm_labels=ltr_lm_labels,
             )
             lm_loss = outputs[0]
             eval_loss += lm_loss.mean().item()