I am trying to make BertForSequenceClassification.from_pretrained() work for multilabel. Since the code I found online is for binary label case.
I have document classification with 12 labels. Using Bert Language model as pytorch model.
what should I do to make it work for multilabel. I get this error, when I run it initially without changing the train/val loop
ValueError: Target size (torch.Size([32])) must be the same as input size (torch.Size([32, 12]))
I assume I have to change the input since the target is [32,12]. But how to do this?
Edit: Full output
======== Epoch 1 / 4 ========
Training...
torch.Size([32, 64])
tensor([[1, 1, 1, ..., 1, 1, 1],
[1, 1, 1, ..., 0, 0, 0],
[1, 1, 1, ..., 0, 0, 0],
...,
[1, 1, 1, ..., 1, 1, 1],
[1, 1, 1, ..., 1, 1, 1],
[1, 1, 1, ..., 1, 1, 1]], device='cuda:0')
tensor([ 9., 9., 3., 8., 9., 10., 4., 3., 4., 4., 9., 0., 9., 9.,
11., 3., 9., 9., 3., 4., 4., 7., 8., 9., 10., 6., 4., 0.,
10., 3., 4., 1.], dtype=torch.float64)
ValueError Traceback (most recent call last)
<ipython-input-25-ac7a3b802ac2> in <module>
90 # Specifically, we'll get the loss (because we provided labels) and the
91 # "logits"--the model outputs prior to activation.
---> 92 result = model(b_input_ids,
93 token_type_ids=None,
94 attention_mask=b_input_mask, 4 frames
/usr/local/lib/python3.8/dist-packages/torch/nn/functional.py in binary_cross_entropy_with_logits(input, target, weight, size_average, reduce, reduction, pos_weight)
3158 3159 if not (target.size() == input.size()):
-> 3160 raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size())) 3161 3162 return torch.binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction_enum)
ValueError: Target size (torch.Size([32])) must be the same as input size (torch.Size([32, 12]))
the code:
from transformers import BertForSequenceClassification, AdamW, BertConfig
# Load BertForSequenceClassification, the pretrained BERT model with a single
# linear classification layer on top.
model = BertForSequenceClassification.from_pretrained(
"bert-base-uncased", # Use the 12-layer BERT model, with an uncased vocab.
num_labels = 2, # The number of output labels--2 for binary classification.
# You can increase this for multi-class tasks.
output_attentions = False, # Whether the model returns attentions weights.
output_hidden_states = False, # Whether the model returns all hidden-states.
)
# Tell pytorch to run this model on the GPU.
model.cuda()
optimizer = AdamW(model.parameters(),
lr = 2e-5, # args.learning_rate - default is 5e-5, our notebook had 2e-5
eps = 1e-8 # args.adam_epsilon - default is 1e-8.
)
from transformers import get_linear_schedule_with_warmup
total_steps = len(train_dataloader) * epochs
# Create the learning rate scheduler.
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps = 0, # Default value in run_glue.py
num_training_steps = total_steps)
import random
import numpy as np
# This training code is based on the `run_glue.py` script here:
# https://github.com/huggingface/transformer/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128
# Set the seed value all over the place to make this reproducible.
seed_val = 42
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
# We'll store a number of quantities such as training and validation loss,
# validation accuracy, and timings.
training_stats = []
# Measure the total training time for the whole run.
total_t0 = time.time()
# For each epoch...
for epoch_i in range(0, epochs):
# ========================================
# Training
# ========================================
# Perform one full pass over the training set.
print("")
print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))
print('Training...')
# Measure how long the training epoch takes.
t0 = time.time()
# Reset the total loss for this epoch.
total_train_loss = 0
# Put the model into training mode. Don't be mislead--the call to
# `train` just changes the *mode*, it doesn't *perform* the training.
# `dropout` and `batchnorm` layers behave differently during training
# vs. test (source: https://stackoverflow.com/questions/51433378/what-does-model-train-do-in-pytorch)
model.train()
# For each batch of training data...
for step, batch in enumerate(train_dataloader):
# Progress update every 40 batches.
if step % 40 == 0 and not step == 0:
# Calculate elapsed time in minutes.
elapsed = format_time(time.time() - t0)
# Report progress.
print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))
# Unpack this training batch from our dataloader.
#
# As we unpack the batch, we'll also copy each tensor to the GPU using the
# `to` method.
#
# `batch` contains three pytorch tensors:
# [0]: input ids
# [1]: attention masks
# [2]: labels
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
# Always clear any previously calculated gradients before performing a
# backward pass. PyTorch doesn't do this automatically because
# accumulating the gradients is "convenient while training RNNs".
# (source: https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch)
model.zero_grad()
# Perform a forward pass (evaluate the model on this training batch).
# In PyTorch, calling `model` will in turn call the model's `forward`
# function and pass down the arguments. The `forward` function is
# documented here:
# https://huggingface.co/transformers/model_doc/bert.html#bertforsequenceclassification
# The results are returned in a results object, documented here:
# https://huggingface.co/transformers/main_classes/output.html#transformers.modeling_outputs.SequenceClassifierOutput
# Specifically, we'll get the loss (because we provided labels) and the
# "logits"--the model outputs prior to activation.
result = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels,
return_dict=True)
loss = result.loss
logits = result.logits
# Accumulate the training loss over all of the batches so that we can
# calculate the average loss at the end. `loss` is a Tensor containing a
# single value; the `.item()` function just returns the Python value
# from the tensor.
total_train_loss += loss.item()
# Perform a backward pass to calculate the gradients.
loss.backward()
# Clip the norm of the gradients to 1.0.
# This is to help prevent the "exploding gradients" problem.
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
# Update parameters and take a step using the computed gradient.
# The optimizer dictates the "update rule"--how the parameters are
# modified based on their gradients, the learning rate, etc.
optimizer.step()
# Update the learning rate.
scheduler.step()
# Calculate the average loss over all of the batches.
avg_train_loss = total_train_loss / len(train_dataloader)
# Measure how long this epoch took.
training_time = format_time(time.time() - t0)
print("")
print(" Average training loss: {0:.2f}".format(avg_train_loss))
print(" Training epcoh took: {:}".format(training_time))
# ========================================
# Validation
# ========================================
# After the completion of each training epoch, measure our performance on
# our validation set.
print("")
print("Running Validation...")
t0 = time.time()
# Put the model in evaluation mode--the dropout layers behave differently
# during evaluation.
model.eval()
# Tracking variables
total_eval_accuracy = 0
total_eval_loss = 0
nb_eval_steps = 0
# Evaluate data for one epoch
for batch in validation_dataloader:
# Unpack this training batch from our dataloader.
#
# As we unpack the batch, we'll also copy each tensor to the GPU using
# the `to` method.
#
# `batch` contains three pytorch tensors:
# [0]: input ids
# [1]: attention masks
# [2]: labels
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
# Tell pytorch not to bother with constructing the compute graph during
# the forward pass, since this is only needed for backprop (training).
with torch.no_grad():
# Forward pass, calculate logit predictions.
# token_type_ids is the same as the "segment ids", which
# differentiates sentence 1 and 2 in 2-sentence tasks.
result = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels,
return_dict=True)
# Get the loss and "logits" output by the model. The "logits" are the
# output values prior to applying an activation function like the
# softmax.
loss = result.loss
logits = result.logits
# Accumulate the validation loss.
total_eval_loss += loss.item()
# Move logits and labels to CPU
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
# Calculate the accuracy for this batch of test sentences, and
# accumulate it over all batches.
total_eval_accuracy += flat_accuracy(logits, label_ids)
# Report the final accuracy for this validation run.
avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)
print(" Accuracy: {0:.2f}".format(avg_val_accuracy))
# Calculate the average loss over all of the batches.
avg_val_loss = total_eval_loss / len(validation_dataloader)
# Measure how long the validation run took.
validation_time = format_time(time.time() - t0)
print(" Validation Loss: {0:.2f}".format(avg_val_loss))
print(" Validation took: {:}".format(validation_time))
# Record all statistics from this epoch.
training_stats.append(
{
'epoch': epoch_i + 1,
'Training Loss': avg_train_loss,
'Valid. Loss': avg_val_loss,
'Valid. Accur.': avg_val_accuracy,
'Training Time': training_time,
'Validation Time': validation_time
}
)
print("")
print("Training complete!")
print("Total training took {:} (h:mm:ss)".format(format_time(time.time()-total_t0)))
