So i am doing my first berttoken classifier. I am using a german polyglot dataset meaning tokenised words and lists of ner labels.
a row is [‘word1’,‘word2’…] [‘ORG’,‘LOC’…]
This is my code
tokenizer = BertTokenizer.from_pretrained('bert-base-german-cased')
encoded_dataset = [tokenizer(item['words'], is_split_into_words=True,return_tensors="pt", padding='max_length', truncation=True, max_length=128) for item in dataset_1]
model = BertForTokenClassification.from_pretrained('bert-base-german-cased', num_labels=1)
for item in encoded_dataset:

    for key in item:

        item[key] = torch.squeeze(item[key])

train_set = encoded_dataset[:500]

test_set = encoded_dataset[500:]

training_args = TrainingArguments(
num_train_epochs=1,

per_device_train_batch_size=4,

per_device_eval_batch_size=4,

output_dir='results',

logging_dir='logs',

no_cuda=False,  # defaults to false anyway, just to be explicit

)

trainer = Trainer(

    model=model,

    tokenizer=tokenizer,

    args=training_args,

    train_dataset=train_set,

)

trainer.train()
And i am getting key error loss