I’m trying to train a Multilabel classification model on Toxic Label classification dataset from Kaggle which has got these labels - toxic,severe_toxic,obscene,threat,insult,identity_hate. I am using Bert-base-cased Huggingface Transformer. Here is the code-
checkpoint = 'bert-base-uncased'
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
Training Data -
train_dataset = tf.data.Dataset.from_tensor_slices((
dict(tokenizer(X_train_list,
None,
padding = True, truncation = True,
return_overflowing_tokens = False,
add_special_tokens = True,
return_attention_mask = True,
return_token_type_ids = True,
return_tensors = 'np'
)),
y_train
))
train_dataset
Validation data -
validation_dataset = tf.data.Dataset.from_tensor_slices((
dict(tokenizer(X_val_list,
None,
padding = True, truncation = True,
return_overflowing_tokens = False,
add_special_tokens = True,
return_attention_mask = True,
return_token_type_ids = True,
return_tensors = 'np')),
y_val
))
validation_dataset
for i in train_dataset.take(1):
print (i)
>>({'input_ids': <tf.Tensor: shape=(512,), dtype=int64, numpy=
array([ 101, 7526, 10086, 2015, 2081, 5310, 18442, 13076, 12392,
2050, 5470, 16407, 3158, 9305, 22556, 8503, 3806, 5444,
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array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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Model -
from tensorflow.keras.optimizers.schedules import PolynomialDecay
from tensorflow.keras.optimizers import Adam
batch_size = 8
num_epochs = 3
#no. of training steps - (toal no. of samples/batch size) * number of epochs
# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied
# by the total number of epochs
num_train_steps = (train_dataset.cardinality().numpy() // batch_size) * num_epochs
lr_scheduler = PolynomialDecay(
initial_learning_rate=5e-5,
end_learning_rate=0.,
decay_steps=num_train_steps
)
opt = Adam(learning_rate=lr_scheduler)
model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=6) # we reinitialize the model
#cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=tf.cast(targets,tf.float32))
loss = tf.keras.losses.BinaryCrossentropy(from_logits= True) #because the model returns logits
model.compile(optimizer=opt, loss=loss, metrics = [tf.keras.metrics.BinaryAccuracy()])
print(model.summary())
history = model.fit(
train_dataset,
validation_data = validation_dataset,
epochs = num_epochs,
batch_size = batch_size,
)
Getting this error -
ValueError Traceback (most recent call last)
<ipython-input-30-a3073a04649b> in <module>()
3 validation_data = validation_dataset,
4 epochs = num_epochs,
----> 5 batch_size = batch_size,
6
7 )
9 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
992 except Exception as e: # pylint:disable=broad-except
993 if hasattr(e, "ag_error_metadata"):
--> 994 raise e.ag_error_metadata.to_exception(e)
995 else:
996 raise
ValueError: in user code:
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:853 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:842 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1286 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2849 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3632 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:835 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:789 train_step
y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py:201 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:141 __call__
losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:245 call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:1809 binary_crossentropy
backend.binary_crossentropy(y_true, y_pred, from_logits=from_logits),
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/backend.py:5000 binary_crossentropy
return tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/nn_impl.py:246 sigmoid_cross_entropy_with_logits_v2
logits=logits, labels=labels, name=name)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/nn_impl.py:133 sigmoid_cross_entropy_with_logits
(logits.get_shape(), labels.get_shape()))
ValueError: logits and labels must have the same shape ((512, 6) vs (6, 1))
Is it because I’m not using an appropriate loss?