Add dense layer on top of Huggingface BERT model
Question:
I want to add a dense layer on top of the bare BERT Model transformer outputting raw hidden-states, and then fine tune the resulting model. Specifically, I am using this base model. This is what the model should do:
- Encode the sentence (a vector with 768 elements for each token of the sentence)
- Keep only the first vector (related to the first token)
- Add a dense layer on top of this vector, to get the desired transformation
So far, I have successfully encoded the sentences:
from sklearn.neural_network import MLPRegressor
import torch
from transformers import AutoModel, AutoTokenizer
# List of strings
sentences = [...]
# List of numbers
labels = [...]
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
model = AutoModel.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
# 2D array, one line per sentence containing the embedding of the first token
encoded_sentences = torch.stack([model(**tokenizer(s, return_tensors='pt'))[0][0][0]
for s in sentences]).detach().numpy()
regr = MLPRegressor()
regr.fit(encoded_sentences, labels)
In this way I can train a neural network by feeding it with the encoded sentences. However, this approach clearly does not fine tune the base BERT model. Can anybody help me? How can I build a model (possibly in pytorch or using the Huggingface library) that can be entirely fine tuned?
Answers:
If you want to tune the BERT model itself you will need to modify the parameters of the model. To do this you will most likely want to do your work with PyTorch. Here is some rough psuedo code to illustrate:
from torch.optim import SGD
model = ... # whatever model you are using
parameters = model.parameters() # or some more specific set of parameters
optimizer = SGD(parameters,lr=.01) # or whatever optimizer you want
optimizer.zero_grad() # boiler-platy pytorch function
input = ... # whatever the appropriate input for your task is
label = ... # whatever the appropriate label for your task is
loss = model(**input, label) # usuall loss is the first item returned
loss.backward() # calculates gradient
optim.step() # runs optimization algorithm
I’ve left out all the relevant details because they are quite tedious and specific to whatever your specific task is. Huggingface has a nice article walking through this is more detail here, and you will definitely want to refer to some pytorch documentation as you use any pytorch stuff. I highly recommend the pytorch blitz before trying to do anything serious with it.
There are two ways to do it: Since you are looking to fine-tune the model for a downstream task similar to classification, you can directly use:
BertForSequenceClassification class. Performs fine-tuning of logistic regression layer on the output dimension of 768.
Alternatively, you can define a custom module, that created a bert model based on the pre-trained weights and adds layers on top of it.
from transformers import BertModel
class CustomBERTModel(nn.Module):
def __init__(self):
super(CustomBERTModel, self).__init__()
self.bert = BertModel.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
### New layers:
self.linear1 = nn.Linear(768, 256)
self.linear2 = nn.Linear(256, 3) ## 3 is the number of classes in this example
def forward(self, ids, mask):
sequence_output, pooled_output = self.bert(
ids,
attention_mask=mask)
# sequence_output has the following shape: (batch_size, sequence_length, 768)
linear1_output = self.linear1(sequence_output[:,0,:].view(-1,768)) ## extract the 1st token's embeddings
linear2_output = self.linear2(linear1_output)
return linear2_output
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
model = CustomBERTModel() # You can pass the parameters if required to have more flexible model
model.to(torch.device("cpu")) ## can be gpu
criterion = nn.CrossEntropyLoss() ## If required define your own criterion
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()))
for epoch in epochs:
for batch in data_loader: ## If you have a DataLoader() object to get the data.
data = batch[0]
targets = batch[1] ## assuming that data loader returns a tuple of data and its targets
optimizer.zero_grad()
encoding = tokenizer.batch_encode_plus(data, return_tensors='pt', padding=True, truncation=True,max_length=50, add_special_tokens = True)
outputs = model(input_ids, attention_mask=attention_mask)
outputs = F.log_softmax(outputs, dim=1)
input_ids = encoding['input_ids']
attention_mask = encoding['attention_mask']
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
For anyone using Tensorflow/ Keras the equivalent of Ashwin’s answer would be:
from tensorflow import keras
from transformers import AutoTokenizer, TFAutoModel
class CustomBERTModel(keras.Model):
def __init__(self):
super(CustomBERTModel, self).__init__()
self.bert = TFAutoModel.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
### New layers:
self.linear1 = keras.layers.Dense(256)
self.linear2 = keras.layers.Dense(3) ## 3 is the number of classes in this example
def call(self, inputs, training=False):
# call expects only one positional argument, so you have to pass in a tuple and unpack. The next parameter is a special reserved training parameter.
ids, mask = inputs
sequence_output = self.bert(ids, mask, training=training).last_hidden_state
# sequence_output has the following shape: (batch_size, sequence_length, 768)
linear1_output = self.linear1(sequence_output[:,0,:]) ## extract the 1st token's embeddings
linear2_output = self.linear2(linear1_output)
return linear2_output
model = CustomBERTModel()
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
ipts = tokenizer("Some input sequence", return_tensors="tf")
test = model((ipts["input_ids"], ipts["attention_mask"]))
Then to train the model you can make a custom training loop using GradientTape.
You can verify that the additional layers are also trainable with model.trainable_weights. You can access weights for individual layers with e.g. model.trainable_weights[-1].numpy() would get the last layer’s bias vector. [Note the Dense layers will only appear after the first time the call method is executed.]
I want to add a dense layer on top of the bare BERT Model transformer outputting raw hidden-states, and then fine tune the resulting model. Specifically, I am using this base model. This is what the model should do:
- Encode the sentence (a vector with 768 elements for each token of the sentence)
- Keep only the first vector (related to the first token)
- Add a dense layer on top of this vector, to get the desired transformation
So far, I have successfully encoded the sentences:
from sklearn.neural_network import MLPRegressor
import torch
from transformers import AutoModel, AutoTokenizer
# List of strings
sentences = [...]
# List of numbers
labels = [...]
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
model = AutoModel.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
# 2D array, one line per sentence containing the embedding of the first token
encoded_sentences = torch.stack([model(**tokenizer(s, return_tensors='pt'))[0][0][0]
for s in sentences]).detach().numpy()
regr = MLPRegressor()
regr.fit(encoded_sentences, labels)
In this way I can train a neural network by feeding it with the encoded sentences. However, this approach clearly does not fine tune the base BERT model. Can anybody help me? How can I build a model (possibly in pytorch or using the Huggingface library) that can be entirely fine tuned?
If you want to tune the BERT model itself you will need to modify the parameters of the model. To do this you will most likely want to do your work with PyTorch. Here is some rough psuedo code to illustrate:
from torch.optim import SGD
model = ... # whatever model you are using
parameters = model.parameters() # or some more specific set of parameters
optimizer = SGD(parameters,lr=.01) # or whatever optimizer you want
optimizer.zero_grad() # boiler-platy pytorch function
input = ... # whatever the appropriate input for your task is
label = ... # whatever the appropriate label for your task is
loss = model(**input, label) # usuall loss is the first item returned
loss.backward() # calculates gradient
optim.step() # runs optimization algorithm
I’ve left out all the relevant details because they are quite tedious and specific to whatever your specific task is. Huggingface has a nice article walking through this is more detail here, and you will definitely want to refer to some pytorch documentation as you use any pytorch stuff. I highly recommend the pytorch blitz before trying to do anything serious with it.
There are two ways to do it: Since you are looking to fine-tune the model for a downstream task similar to classification, you can directly use:
BertForSequenceClassification class. Performs fine-tuning of logistic regression layer on the output dimension of 768.
Alternatively, you can define a custom module, that created a bert model based on the pre-trained weights and adds layers on top of it.
from transformers import BertModel
class CustomBERTModel(nn.Module):
def __init__(self):
super(CustomBERTModel, self).__init__()
self.bert = BertModel.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
### New layers:
self.linear1 = nn.Linear(768, 256)
self.linear2 = nn.Linear(256, 3) ## 3 is the number of classes in this example
def forward(self, ids, mask):
sequence_output, pooled_output = self.bert(
ids,
attention_mask=mask)
# sequence_output has the following shape: (batch_size, sequence_length, 768)
linear1_output = self.linear1(sequence_output[:,0,:].view(-1,768)) ## extract the 1st token's embeddings
linear2_output = self.linear2(linear1_output)
return linear2_output
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
model = CustomBERTModel() # You can pass the parameters if required to have more flexible model
model.to(torch.device("cpu")) ## can be gpu
criterion = nn.CrossEntropyLoss() ## If required define your own criterion
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()))
for epoch in epochs:
for batch in data_loader: ## If you have a DataLoader() object to get the data.
data = batch[0]
targets = batch[1] ## assuming that data loader returns a tuple of data and its targets
optimizer.zero_grad()
encoding = tokenizer.batch_encode_plus(data, return_tensors='pt', padding=True, truncation=True,max_length=50, add_special_tokens = True)
outputs = model(input_ids, attention_mask=attention_mask)
outputs = F.log_softmax(outputs, dim=1)
input_ids = encoding['input_ids']
attention_mask = encoding['attention_mask']
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
For anyone using Tensorflow/ Keras the equivalent of Ashwin’s answer would be:
from tensorflow import keras
from transformers import AutoTokenizer, TFAutoModel
class CustomBERTModel(keras.Model):
def __init__(self):
super(CustomBERTModel, self).__init__()
self.bert = TFAutoModel.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
### New layers:
self.linear1 = keras.layers.Dense(256)
self.linear2 = keras.layers.Dense(3) ## 3 is the number of classes in this example
def call(self, inputs, training=False):
# call expects only one positional argument, so you have to pass in a tuple and unpack. The next parameter is a special reserved training parameter.
ids, mask = inputs
sequence_output = self.bert(ids, mask, training=training).last_hidden_state
# sequence_output has the following shape: (batch_size, sequence_length, 768)
linear1_output = self.linear1(sequence_output[:,0,:]) ## extract the 1st token's embeddings
linear2_output = self.linear2(linear1_output)
return linear2_output
model = CustomBERTModel()
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
ipts = tokenizer("Some input sequence", return_tensors="tf")
test = model((ipts["input_ids"], ipts["attention_mask"]))
Then to train the model you can make a custom training loop using GradientTape.
You can verify that the additional layers are also trainable with model.trainable_weights. You can access weights for individual layers with e.g. model.trainable_weights[-1].numpy() would get the last layer’s bias vector. [Note the Dense layers will only appear after the first time the call method is executed.]