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Learn how to use BERT to calculate the semantic similarity between two texts
Ever since its inception in 2017 by Google Mind workforce, Transformers have quickly change into the state-of-the-art mannequin for varied use circumstances inside the fields of Pc Imaginative and prescient and NLP. Its superior efficiency led to the event of a number of state-of-the-art fashions corresponding to BERT and its variants like distilBERT and RoBERTa.
BERT outperformed outdated recurrent fashions in varied NLP duties corresponding to textual content classification, Named Entity Recognition (NER), query answering, and even the duty that we’re going to concentrate on on this article, which is semantic textual similarity (STS).
Thus, on this article, we’re going to see how we are able to prepare a BERT mannequin for STS job with the assistance of Sentence Transformers library. Subsequent, we’re going to make use of the educated mannequin to foretell unknown information. However as a starter, we have to know first what STS job truly is and the dataset that we are going to use for this job.
Semantic textual similarity (STS) refers to a job wherein we evaluate the similarity between one textual content to a different.
The output that we get from a mannequin for STS job is often a floating quantity indicating the similarity between two texts being in contrast.
There are a number of methods to quantify the similarity between a pair of texts. Let’s check out the dataset that we’re going to make use of on this article for instance, which is the STSB dataset (licensed underneath CC-Share Alike 4.0).
!pip set up datasetsfrom datasets import load_dataset
dataset = load_dataset("stsb_multi_mt", identify="en", cut up="prepare")
print(dataset[0])
>>> {'sentence1': 'A aircraft is taking off.',
'An air aircraft is taking off.',
'similarity_score': 5.0}
print(dataset[1])
>>> {'sentence1': 'A person is taking part in a big flute.',
'sentence2': 'A person is taking part in a flute.',
'similarity_score': 3.799999952316284}
The similarity between a pair of texts is labeled between a quantity from 1 to five; 1 if a pair of texts is totally dissimilar, and 5 if a pair of texts is strictly related when it comes to their semantic that means.
Nonetheless, there’s a catch. Once we need to prepare a BERT mannequin with the assistance of Sentence Transformers library, we have to normalize the similarity rating such that it has a spread between 0 to 1. This may be achieved just by dividing every similarity rating by 5.
similarity = [i['similarity_score'] for i in dataset]
normalized_similarity = [i/5.0 for i in similarity]
Now that we all know the dataset that we are going to be working with, let’s now proceed to the mannequin that we’re going to make use of on this article.
Transformers-based fashions corresponding to BERT, distilBERT, or RoBERTa anticipate a sequence of tokens as enter. Thus, the very first step that needs to be carried out is to transform our enter textual content right into a sequence of tokens. This course of known as tokenization.
The tokenization course of for BERT fashions consists of two steps. First, our enter textual content might be cut up into a number of small chunks known as tokens; one token is usually a phrase or a sub-word. Second, two particular tokens are added to our sequence of tokens: one in the beginning and one on the finish. These two particular tokens are:
- [CLS]: that is the primary token in every sequence of token
- [SEP]: this token is essential to provide BERT a touch about which token belongs to which sequence. If there is just one sequence of tokens, then this token would be the final token within the sequence
Relying on the utmost sequence size of the tokenizer that you just outline prematurely, a bunch of [PAD] tokens may even be appended after the [SEP] token.
The tokenized enter then might be handed into the mannequin and because the output, we’ll get the embedding vector of every token. Every embedding vector has 768 dimensions.
If we use BERT for classification functions, then usually we take the embedding vector of the [CLS] token and cross it to a softmax or sigmoid layer ultimately that may act as a classifier.
If we use BERT for STS job, the workflow could be one thing like this:
With the workflow proven above, BERT achieved state-of-the-art efficiency on the STS benchmark. Nonetheless, there’s one main downside to that workflow: the scalability issue.
Think about we’ve got a model new textual content. Subsequent, we need to question essentially the most related entry to this new textual content in our database which consists of 100K completely different texts. If we use BERT structure as above, then we have to evaluate our new textual content with every entry in our database 100K instances. This implies 100K instances of tokenization course of and ahead cross.
The primary drawback of this scalability issue is the truth that BERT outputs the embedding vector of every token and never the embedding vector of every textual content/sentence.
If BERT someway may give us a significant sentence-level embedding, then we are able to save the embedding of every entry in our database. As soon as we’ve got a brand new textual content, then we solely want to check the sentence embedding of our new textual content with every entry’s sentence embedding in our database with the assistance of cosine similarity, which is a method quicker technique.
That is what Sentence BERT (SBERT) tries to sort out. You’ll be able to view SBERT as a fine-tuned model of BERT by making use of siamese-type mannequin structure, as you’ll be able to see beneath:
The issue with the structure above is that it nonetheless generates token-level embedding. Thus, SBERT implements a further pooling layer on prime of BERT. There are three completely different pooling methods applied by SBERT:
- Utilizing the embedding of [CLS] token
- Utilizing the imply of all token-level embedding vectors (that is the default implementation)
- Utilizing the max-over-time token-level embedding vectors
The illustration above is the ultimate structure of SBERT mannequin. What we get after the pooling layer is the embedding vector of a textual content that has 768 dimensions. This embedding then might be in contrast to one another with pairwise distance or cosine similarity, which is strictly what STS job is all about.
To implement SBERT, we are able to use sentence-transformers library. When you haven’t put in it but, you are able to do so through pip:
!pip set up sentence-transformers
Now we’re going to implement SBERT mannequin primarily based on BERT, however you too can implement SBERT with BERT variants like distilBERT or RoBERTa, and even load a mannequin that has been pretrained on explicit dataset. You could find all the available models here.
from sentence_transformers import SentenceTransformer, fashionsword_embedding_model = fashions.Transformer('bert-base-uncased', max_seq_length=128)
pooling_model = fashions.Pooling(word_embedding_model.get_word_embedding_dimension())
sts_bert_model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
From the code snippet above, we first load a BERT mannequin as our phrase embedding mannequin, after which we apply a pooling layer on prime of the BERT mannequin to acquire the sentence-level embedding ultimately.
Let’s say that we’ve got a pair of sentences and we need to fetch the sentence-level embedding of every sentence. We are able to accomplish that by doing the next:
!pip set up transformersfrom transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
sentence_1 = [i['sentence1'] for i in dataset]
sentence_2 = [i['sentence2'] for i in dataset]
text_cat = [[str(x), str(y)] for x,y in zip(sentence_1, sentence_2)][0]
input_data = tokenizer(text_cat, padding='max_length', max_length = 128, truncation=True, return_tensors="pt")
output = sts_bert_model(input_data)
print(output['sentence_embedding'][0].measurement())
>>> torch.Dimension([768])
print(output['sentence_embedding'][1].measurement())
>>> torch.Dimension([768])
On this part, we’re going to coach an SBERT mannequin on the dataset that we’ve mentioned within the earlier part for STS job.
Mannequin Structure Definition
Let’s outline the mannequin structure first.
import torchclass STSBertModel(torch.nn.Module):
def __init__(self):
tremendous(STSBertModel, self).__init__()
word_embedding_model = fashions.Transformer('bert-base-uncased', max_seq_length=128)
pooling_model = fashions.Pooling(word_embedding_model.get_word_embedding_dimension())
self.sts_model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
def ahead(self, input_data):
output = self.sts_model(input_data)
return output
The mannequin structure above is much like what we’ve seen within the earlier part. We use a BERT base mannequin as our phrase embedding mannequin. The output of this mannequin continues to be a token-level embedding. Thus, we have to add a pooling layer on prime of it.
The ultimate output that we get from our SBERT mannequin above is 768 dimensions of sentence-level embedding vector. Because the enter of our mannequin is a pair of texts, then the output may even be a pair of 768 dimensions of sentence-level embedding vector.
Knowledge Loader
Knowledge loader is important to create batch on our dataset. This course of is essential as a result of we are able to’t simply feed our mannequin with the entire dataset directly throughout the coaching course of.
class DataSequence(torch.utils.information.Dataset):def __init__(self, dataset):
similarity = [i['similarity_score'] for i in dataset]
self.label = [i/5.0 for i in similarity]
self.sentence_1 = [i['sentence1'] for i in dataset]
self.sentence_2 = [i['sentence2'] for i in dataset]
self.text_cat = [[str(x), str(y)] for x,y in zip(self.sentence_1, self.sentence_2)]
def __len__(self):
return len(self.text_cat)
def get_batch_labels(self, idx):
return torch.tensor(self.label[idx])
def get_batch_texts(self, idx):
return tokenizer(self.text_cat[idx], padding='max_length', max_length = 128, truncation=True, return_tensors="pt")
def __getitem__(self, idx):
batch_texts = self.get_batch_texts(idx)
batch_y = self.get_batch_labels(idx)
return batch_texts, batch_y
def collate_fn(texts):
num_texts = len(texts['input_ids'])
options = record()
for i in vary(num_texts):
options.append({'input_ids':texts['input_ids'][i], 'attention_mask':texts['attention_mask'][i]})
return options
We’ve seen within the above sections what our dataset appears to be like like and the best way to put together it in order that it may be utilized by our mannequin for STS job. The code above does precisely the identical:
- The similarity rating between every pair of texts is normalized, and this might be our floor fact label for mannequin coaching
- Every pair of texts is tokenized with the very same tokenizer and the very same step that we noticed within the earlier part. The tokenized pair of texts would be the enter of our mannequin throughout coaching.
The collate_fn above is a vital operate to group every pair of texts collectively after the tokenization course of for batching functions.
Loss Operate
In an STS job, our purpose is to coach a mannequin such that it might probably distinguish between related and dissimilar pairs of texts when it comes to their semantic that means. Which means we wish the mannequin to push the gap of dissimilar pairs of texts far aside, while maintaining the gap of comparable ones shut to one another.
There are just a few frequent loss features that we are able to use to attain this goal: cosine similarity loss, triplet loss, and contrastive loss.
Usually we are able to use contrastive loss for this case. Nonetheless, contrastive loss expects our label to be binary, i.e the label is 1 if the pair is semantically related, and 0 in any other case. In the meantime, what we’ve got because the label on this dataset is a floating quantity that ranges between 0 to 1, thus cosine similarity loss could be a greater loss operate to implement.
class CosineSimilarityLoss(torch.nn.Module):def __init__(self, loss_fct = torch.nn.MSELoss(), cos_score_transformation=torch.nn.Id()):
tremendous(CosineSimilarityLoss, self).__init__()
self.loss_fct = loss_fct
self.cos_score_transformation = cos_score_transformation
self.cos = torch.nn.CosineSimilarity(dim=1)
def ahead(self, enter, label):
embedding_1 = torch.stack([inp[0] for inp in enter])
embedding_2 = torch.stack([inp[1] for inp in enter])
output = self.cos_score_transformation(self.cos(embedding_1, embedding_2))
return self.loss_fct(output, label.squeeze())
This loss operate takes the sentence-level embedding of every textual content, after which it computes the cosine similarity between the 2 embeddings. Because of this, the loss operate will push dissimilar pairs far other than one another within the vector house, while maintaining the same pairs shut to one another.
Mannequin Coaching
Now that we’ve got arrange the mannequin’s structure, the information loader, and the loss operate, it’s time for us to coach the mannequin. The code is simply a normal Pytorch coaching script, as you’ll be able to see beneath:
from torch.optim import Adam
from torch.utils.information import DataLoader
from tqdm import tqdmdef model_train(dataset, epochs, learning_rate, bs):
use_cuda = torch.cuda.is_available()
system = torch.system("cuda" if use_cuda else "cpu")
mannequin = STSBertModel()
criterion = CosineSimilarityLoss()
optimizer = Adam(mannequin.parameters(), lr=learning_rate)
train_dataset = DataSequence(dataset)
train_dataloader = DataLoader(train_dataset, num_workers=4, batch_size=bs, shuffle=True)
if use_cuda:
mannequin = mannequin.cuda()
criterion = criterion.cuda()
best_acc = 0.0
best_loss = 1000
for i in vary(epochs):
total_acc_train = 0
total_loss_train = 0.0
for train_data, train_label in tqdm(train_dataloader):
train_data['input_ids'] = train_data['input_ids'].to(system)
train_data['attention_mask'] = train_data['attention_mask'].to(system)
del train_data['token_type_ids']
train_data = collate_fn(train_data)
output = [model(feature)['sentence_embedding'] for characteristic in train_data]
loss = criterion(output, train_label.to(system))
total_loss_train += loss.merchandise()
loss.backward()
optimizer.step()
optimizer.zero_grad()
print(f'Epochs: {i + 1} | Loss: {total_loss_train / len(dataset): .3f}')
mannequin.prepare()
return mannequin
EPOCHS = 8
LEARNING_RATE = 1e-6
BATCH_SIZE = 8
# Practice the mannequin
trained_model = model_train(dataset, EPOCHS, LEARNING_RATE, BATCH_SIZE)
Within the implementation above, we prepare our mannequin for 8 epochs , the educational charge is about to 10e-6, and the batch measurement is about to eight. These are hyperparameters you can mess around to fit your personal want.
When you run the model_train operate above, you’ll get a coaching progress that appears one thing like this:
Mannequin Prediction
After we educated our mannequin, now we are able to use it to foretell unseen information, i.e an unseen pair of texts. Nonetheless, earlier than we feed the mannequin with an unseen pair of texts, let’s create a operate that allows us to acquire the similarity prediction from our mannequin.
# Load check information
test_dataset = load_dataset("stsb_multi_mt", identify="en", cut up="check")# Put together check information
sentence_1_test = [i['sentence1'] for i in test_dataset]
sentence_2_test = [i['sentence2'] for i in test_dataset]
text_cat_test = [[str(x), str(y)] for x,y in zip(sentence_1_test, sentence_2_test)]
# Operate to foretell check information
def predict_sts(texts):
trained_model.to('cpu')
trained_model.eval()
test_input = tokenizer(texts, padding='max_length', max_length = 128, truncation=True, return_tensors="pt")
test_input['input_ids'] = test_input['input_ids']
test_input['attention_mask'] = test_input['attention_mask']
del test_input['token_type_ids']
test_output = trained_model(test_input)['sentence_embedding']
sim = torch.nn.purposeful.cosine_similarity(test_output[0], test_output[1], dim=0).merchandise()
return sim
The code implementation above consists of all the preprocessing steps of the information in addition to the steps to fetch the mannequin’s prediction.
Let’s say that we’ve got an identical pair of texts as might be seen beneath:
print(text_cat_test[420])
>>> ['four children are playing on a trampoline.',
'Four kids are jumping on a trampoline.']print(predict_sts(text_cat_test[420]))
>>> 0.8608950972557068
Now we are able to simply name predict_sts operate and we get the cosine similarity between two texts inferred by our mannequin. On this case, we get a similarity of 0.860. Which means our pair of texts are similar to one another.
For comparability, let’s now feed the mannequin with a pair of dissimilar texts.
print(text_cat_test[245])
>>> ['A man spins on a surf board.',
'A man is putting barbecue sauce on chicken.']print(predict_sts(text_cat_test[245]))
>>> 0.05531075596809387
As you’ll be able to see above, when we’ve got a pair of dissimilar texts, the similarity is simply 0.055, which signifies that embedding between two texts within the vector house is much other than one another. And that is precisely what our mannequin has been educated for.
On this article, we’ve got applied a BERT mannequin for a semantic textual similarity job. Particularly, we used Sentence-Transformers library to fine-tune a BERT mannequin into Siamese structure such that we’re in a position to get the sentence-level embedding for every textual content. The sentence-level embedding for every textual content then might be in contrast to one another through cosine similarity.
You could find all the code applied on this article in this notebook.
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