Training Fails with RuntimeError related to wrong data type
I am new to Hugging Face and transformers in general. I have worked with tensorflow models before, but not with Pytorch.I want to create a regression model based on DNA, and have found an interesting model here:armheb/DNA_bert_4.Loading the model, tokenizing the data etc works, however when I want to train the model, I recive a runtime error leading back to loss.backwards(), stating that it found double but expected float.I then thought it might be the regression and the custom loss function I wanted to use, so I created random class (0 or 1) for my data, and copied the accuracy loss function from the Hugging Face tutorial, resulting in the same error, this time with long and float.Therefor i assume I have made a mistake with creating the labels/loss function, however I do not know how to fix it.Here is my current code.import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer
from transformers import pipeline

import numpy as np
model_name="armheb/DNA_bert_4"



model = AutoModelForSequenceClassification.from_pretrained(model_name,num_labels=1)

tokenizer = AutoTokenizer.from_pretrained(model_name)

#reading in data, truncated for this forum post

df_whole_genome=pd.read_csv("xyz",sep="\t",index_col=0)




batch=tokenizer(list(df_whole_genome["Sequence"].values),padding="max_length")
labels=df_whole_genome.value_of_interest.values

class CycDataset(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)

dataset=CycDataset(batch,labels)

train, test = train_test_split(dataset, test_size=0.2,random_state=42)here is the trainingfrom sklearn.metrics import mean_squared_error


def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    rmse = mean_squared_error(labels, predictions, squared=False)
    return {"rmse": rmse}



training_args = TrainingArguments(output_dir="test_trainer",
  num_train_epochs=3
    )



trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train,
    eval_dataset=test,
    compute_metrics=compute_metrics,
)
trainer.train()This results in***** Running training *****
  Num examples = 65923
  Num Epochs = 3
  Instantaneous batch size per device = 8
  Total train batch size (w. parallel, distributed & accumulation) = 8
  Gradient Accumulation steps = 1
  Total optimization steps = 24723
Traceback (most recent call last):

  File "/tmp/ipykernel_8483/462224225.py", line 21, in <cell line: 21>
    trainer.train()

  File "redacted/envs/hugging/lib/python3.8/site-packages/transformers/trainer.py", line 1332, in train
    tr_loss_step = self.training_step(model, inputs)

  File "redacted/envs/hugging/lib/python3.8/site-packages/transformers/trainer.py", line 1909, in training_step
    loss.backward()

  File "redacted/anaconda3/envs/hugging/lib/python3.8/site-packages/torch/tensor.py", line 221, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)

  File "redacted/anaconda3/envs/hugging/lib/python3.8/site-packages/torch/autograd/__init__.py", line 130, in backward
    Variable._execution_engine.run_backward(

RuntimeError: Found dtype Double but expected FloatThe labels as I call them here are float64
2022-05-06T13:00:45Z
i tried the same with the example from the hugging face tutorial (yelp reviews). If I try to make it a single class problem/regression, the same error occures.model.train()
for epoch in range(num_epochs):
    for batch in train_dataloader:
        batch = {k: v.to(device) for k, v in batch.items()}
        outputs = model(**batch)
        loss = outputs.loss
        loss.backward()

        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()Writing my own training loop fails at the loss.backward() step (as the trainer does) with wrong datatype.this happens if I specifiy num_labels=1 and or problem_type=“regression”transformers version = 4.15tokenizers version=0.10.1pytorch version=1.11.0