Sorry, here is the code that produces the error: “Indexing with integers (to access backend Encoding for a given batch index) is not available when using Python based tokenizers”
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from sklearn.model_selection import train_test_split
from transformers import BartTokenizer
tokenizer = BartTokenizer.from_pretrained(‘facebook/bart-large-cnn’)
train_texts, val_texts, train_labels, val_labels = train_test_split(df.articles, df.highlights, test_size=.2)
train_texts = train_texts.values.tolist()
train_labels = train_labels.values.tolist()
val_texts = val_texts.values.tolist()
val_labels = val_labels.values.tolist()
train_encodings = tokenizer(train_texts, truncation=True, padding=True)
train_label_encodings = tokenizer(train_labels, truncation=True, padding=True)
val_encodings = tokenizer(val_texts, truncation=True, padding=True)
val_label_encodings = tokenizer(val_labels, truncation=True, padding=True)
import torch
class PyTorchDatasetCreate(torch.utils.data.Dataset):
def init(self, encodings, labels):
self.encodings = encodings
self.labels = labels
def __getitem__(self, idx):
    item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
    item['labels'] = torch.tensor(self.labels[idx])
    return item

def __len__(self):
    return len(self.labels)

train_dataset = PyTorchDatasetCreate(train_encodings, train_label_encodings)
val_dataset = PyTorchDatasetCreate(val_encodings, val_label_encodings)
from transformers import BartForConditionalGeneration, Trainer, TrainingArguments
training_args = TrainingArguments(
output_dir=’./results’,          # output directory
num_train_epochs=3,              # total number of training epochs
per_device_train_batch_size=1,  # batch size per device during training
per_device_eval_batch_size=1,   # batch size for evaluation
warmup_steps=200,                # number of warmup steps for learning rate scheduler
weight_decay=0.01,               # strength of weight decay
logging_dir=’./logs’,            # directory for storing logs
logging_steps=10)
model = BartForConditionalGeneration.from_pretrained(“sshleifer/distilbart-cnn-12-6”)
trainer = Trainer(
model=model,                         # the instantiated  Transformers model to be trained
args=training_args,                  # training arguments, defined above
train_dataset=train_dataset,         # training dataset
eval_dataset=val_dataset)             # evaluation dataset
trainer.train()
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The code for the other error ("TypeError: new(): invalid data type ‘str’) is identical except the labels (train and val) are not tokenized.  Appreciate your help!