I try to use the tokenizer method to tokenize the sentence and then mean pool the attention mask to get the vectors for each sentence. However, the current default size embedding is 768 and I wish to reduce it to 200 instead but failed. below is my code.
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/bert-base-nli-mean-tokens')
model = AutoModel.from_pretrained('sentence-transformers/bert-base-nli-mean-tokens')
model.resize_token_embeddings(200)
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
Error:
2193 # Note [embedding_renorm set_grad_enabled]
2194 # XXX: equivalent to
2195 # with torch.no_grad():
2196 # torch.embedding_renorm_
2197 # remove once script supports set_grad_enabled
2198 _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 2199 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
IndexError: index out of range in self
my expected output is:
when use:
print(len(sentence_embeddings[0]))
-> 200
I think you've misunderstood the resize_token_embeddings. According to docs It
Resizes input token embeddings matrix of the model if new_num_tokens != >config.vocab_size.
Takes care of tying weights embeddings afterwards if the model class has a >tie_weights() method.
meaning it is used when you add/remove tokens from vocabulary. Here resizing refers to resizing the token->embedding dictionary.
I guess what you want to do is change the hidden_size of bert model. In order to do that you have to change the hidden_size in config.json which will re-initialize all weights and you have to re-train everything i.e very computationally expensive.
I think your best option is to add a linear layer on top of BertModel of dimension (768x200) and fine-tune on your downstream task.
Related
I am trying to use the bert-large-uncased for long sequence ending, but it's giving the error:
Code:
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')
model = BertModel.from_pretrained("bert-large-uncased")
text = "Replace me by any text you'd like."*1024
encoded_input = tokenizer(text, truncation=True, max_length=1024, return_tensors='pt')
output = model(**encoded_input)
It's giving the following error :
~/.local/lib/python3.6/site-packages/transformers/models/bert/modeling_bert.py in forward(self, input_ids, token_type_ids, position_ids, inputs_embeds, past_key_values_length)
218 if self.position_embedding_type == "absolute":
219 position_embeddings = self.position_embeddings(position_ids)
--> 220 embeddings += position_embeddings
221 embeddings = self.LayerNorm(embeddings)
222 embeddings = self.dropout(embeddings)
RuntimeError: The size of tensor a (1024) must match the size of tensor b (512) at non-singleton dimension 1
I also tried to change the default size of the positional embedding:
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')
model = BertModel.from_pretrained("bert-large-uncased")
model.config.max_position_embeddings = 1024
text = "Replace me by any text you'd like."*1024
encoded_input = tokenizer(text, truncation=True, max_length=1024, return_tensors='pt')
output = model(**encoded_input)
But still the error is persistent, How to use large model for 1024 length sequences?
I might be wrong, but I think you already have your answers here: How to use Bert for long text classification?
Basically you will need some kind of truncation on your text, or you will need to handle it in chunks, and stick them back together.
Side note: large model is not called large because of the sequence length. Max sequence length will be still 512 tokens. (tokens from your tokenizer, not words in your sentence)
EDIT:
The pretrained model you would like to use is trained on a maximum of 512 tokens. When you download it from huggingface, you can see max_position_embeddings in the configuration, which is 512. That means that you can not really extend on this. (actually that is not true)
However you can always tweak your configurations.
tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')
model = BertModel.from_pretrained(
'bert-large-uncased',
max_position_embeddings=1024,
ignore_mismatched_sizes=True
)
Note, that this is very ill-advised since it will ruin your pretrained model. Maybe it will go rouge, planets start to collide, or pigs will start to fall out of the skies. No one can really tell. Use it at your own risk.
I am trying to get the embeddings from pre-trained wav2vec2 models (e.g., from jonatasgrosman/wav2vec2-large-xlsr-53-german) using my own dataset.
My aim is to use these features for a downstream task (not specifically speech recognition). Namely, since the dataset is relatively small, I would train an SVM with these embeddings for the final classification.
So far I have tried this:
model_name = "facebook/wav2vec2-large-xlsr-53-german"
feature_extractor = Wav2Vec2Processor.from_pretrained(model_name)
model = Wav2Vec2Model.from_pretrained(model_name)
input_values = feature_extractor(train_dataset[:10]["speech"], return_tensors="pt", padding=True,
feature_size=1, sampling_rate=16000 ).input_values
Then, I am not sure whether the embeddings here correspond to the sequence of last_hidden_states:
hidden_states = model(input_values).last_hidden_state
or to the sequence of features of the last conv layer of the model:
features_last_cnn_layer = model(input_values).extract_features
Also, is this the correct way to extract features from a pre-trained model?
How one can get embeddings from a specific layer?
PD: Posting here as the HuggingFace's forum seems to be less active.
Just check the documentation:
last_hidden_state (torch.FloatTensor of shape (batch_size,
sequence_length, hidden_size)) – Sequence of hidden-states at the
output of the last layer of the model.
extract_features (torch.FloatTensor of shape (batch_size,
sequence_length, conv_dim[-1])) – Sequence of extracted feature
vectors of the last convolutional layer of the model.
The last_hidden_state vector represents so called contextualized embeddings (i.e. every feature (CNN output) has a vector representation that is to some extend influenced by the other tokens of the sequence).
The extract_features vector represents the embeddings of your input (after the CNNs).
.
Also, is this the correct way to extract features from a pre-trained
model?
Yes.
How one can get embeddings from a specific layer?
Set output_hidden_states=True:
o = model(input_values,output_hidden_states=True)
o.keys()
Output:
odict_keys(['last_hidden_state', 'extract_features', 'hidden_states'])
The hidden_states value contains the embeddings and the contextualized embeddings of each attention layer.
P.S.: jonatasgrosman/wav2vec2-large-xlsr-53-german model was trained with feat_extract_norm==layer. That means, you should also pass an attention mask to the model:
model_name = "facebook/wav2vec2-large-xlsr-53-german"
feature_extractor = Wav2Vec2Processor.from_pretrained(model_name)
model = Wav2Vec2Model.from_pretrained(model_name)
i= feature_extractor(train_dataset[:10]["speech"], return_tensors="pt", padding=True,
feature_size=1, sampling_rate=16000 )
model(**i)
I have downloaded the glove trained matrix and used it in a Keras layer. however, I need the sentence embedding for another task.
I want to calculate the mean of all the word embeddings that are in that sentence.
what is the most efficient way to do that since there are about 25000 sentences?
also, I don't want to use a Lambda layer in Keras to get the mean of them.
the best way to do this is to use a GlobalAveragePooling1D layer. it receives the embeddings of tokens inside the sentences from the Embedding layer with the shapes (n_sentence, n_token, emb_dim) and computes the average of each token present in the sentence. the result has shape (n_sentence, emb_dim)
here a code example
embedding_dim = 128
vocab_size = 100
sentence_len = 20
embedding_matrix = np.random.uniform(-1,1, (vocab_size,embedding_dim))
test_sentences = np.random.randint(0,vocab_size, (3,sentence_len))
inp = Input((sentence_len))
embedder = Embedding(vocab_size, embedding_dim,
trainable=False, weights=[embedding_matrix])(inp)
avg = GlobalAveragePooling1D()(embedder)
model = Model(inp, avg)
model.summary()
model(test_sentences) # the mean of all the word embeddings inside sentences
Prior to passing my tokens through BERT, I would like to perform some processing on their embeddings, (the result of the embedding lookup layer). The HuggingFace BERT TensorFlow implementation allows us to access the output of embedding lookup using:
import tensorflow as tf
from transformers import BertConfig, BertTokenizer, TFBertModel
bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
input_ids = tf.constant(bert_tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :]
attention_mask = tf.stack([tf.ones(shape=(len(sent),)) for sent in input_ids])
token_type_ids = tf.stack([tf.ones(shape=(len(sent),)) for sent in input_ids])
config = BertConfig.from_pretrained('bert-base-uncased', output_hidden_states=True)
bert_model = TFBertModel.from_pretrained('bert-base-uncased', config=config)
result = bert_model(inputs={'input_ids': input_ids,
'attention_mask': attention_mask,
'token_type_ids': token_type_ids})
inputs_embeds = result[-1][0] # output of embedding lookup
Subsequently, one can process inputs_embeds and then send this in as an input to the same model using:
inputs_embeds = process(inputs_embeds) # some processing on inputs_embeds done here (dimensions kept the same)
result = bert_model(inputs={'inputs_embeds': inputs_embeds,
'attention_mask': attention_mask,
'token_type_ids': token_type_ids})
output = result[0]
where output now contains the output of BERT for the modified input. However, this requires two full passes through BERT. Instead of running BERT all the way through just to perform embedding lookup, I would like to just get the output of the embedding lookup layer. Is this possible, and if so, how?
It is in fact incorrect to treat the first output result[-1][0] as the result of an embedding lookup. The raw embedding lookup is given by:
embeddings = bert_model.bert.get_input_embeddings()
word_embeddings = embeddings.word_embeddings
inputs_embeds = tf.gather(word_embeddings, input_ids)
while result[-1][0] gives the embedding lookup plus positional embeddings and token type embeddings. The above code does not require a full pass through BERT, and the result can be processed prior to feeding into the remaining layers of BERT.
EDIT: To get the result of addition positional and token type embeddings to an arbitrary inputs_embeds, one can use:
full_embeddings = embeddings(inputs=[None, None, token_type_ids, inputs_embeds])
Here, the call method for the embeddings object accepts a list which is fed into the _embeddings method. The first value is input_ids, the second position_ids, the third token_type_ids, and the fourth inputs_embeds. (See here for more details.) If you have multiple sentences in one input, you may need to set position_ids.
Hugging Face documentation describes how to do a sequence classification using a Bert model:
from transformers import BertTokenizer, BertForSequenceClassification
import torch
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, logits = outputs[:2]
However, there is only example for batch size 1. How to implement it when we have a list of phrases and want to use a bigger batch size?
in that example unsqueeze is used to add a dimension to the input/labels, so that it is an array of size (batch_size, sequence_length). If you want to use a batch size > 1, you can build an array of sequences instead, like in the following example:
from transformers import BertTokenizer, BertForSequenceClassification
import torch
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
sequences = ["Hello, my dog is cute", "My dog is cute as well"]
input_ids = torch.tensor([tokenizer.encode(sequence, add_special_tokens=True) for sequence in sequences])
labels = torch.tensor([[1], [0]]) # Labels depend on the task
outputs = model(input_ids, labels=labels)
loss, logits = outputs[:2]
In that example, both sequences get encoded in the same number of tokens so it's easy to build a tensor containing both sequences, but if they have a differing amount of elements you would need to pad the sequences and tell the model which tokens it should attend to (so that it ignores the padded values) using an attention mask.
There is an entry in the glossary concerning attention masks which explains their purpose and usage. You pass this attention mask to the model when calling its forward method.