Bertje Next Sentence

Next Sentence Prediction with BERTje for Dutch

This guide explains how to use the pre-trained BERTje model to perform Next Sentence Prediction (NSP) in Dutch. NSP is a task that determines whether one sentence logically follows another.

Understanding Next Sentence Prediction (NSP)

Next Sentence Prediction is a fundamental task in understanding the relationship between sentences. In the context of BERT-like models, NSP helps the model learn contextual relationships between text segments. For BERT, this task was crucial during its pre-training phase to enable it to handle tasks requiring sentence-level understanding, such as Question Answering and Natural Language Inference.

Performing NSP with BERTje

This section details the steps involved in using BERTje for NSP with the Hugging Face Transformers library.

1. Import Required Libraries

First, import the necessary classes from the transformers library and the torch.nn.functional module.

from transformers import BertForNextSentencePrediction, BertTokenizer
from torch.nn.functional import softmax
import torch

2. Load Pre-trained BERTje Model and Tokenizer

We will load the bert-base-dutch-cased model, which is a pre-trained BERT model specifically for the Dutch language.

model = BertForNextSentencePrediction.from_pretrained("wietsedv/bert-base-dutch-cased")
tokenizer = BertTokenizer.from_pretrained("wietsedv/bert-base-dutch-cased")

3. Define Sentence Pairs for Prediction

Prepare the two sentences you want to evaluate for their sequential relationship.

sentence_A = 'Ik woon in Amsterdam.'
sentence_B = 'Een geweldige plek om te zijn.'

Example with a non-following sentence:

sentence_C = 'De kat zat op de mat.'

4. Tokenize Sentence Pairs and Get Embeddings

Tokenize the sentence pairs using the BERTje tokenizer. The tokenizer converts the text into a format suitable for the model, adding special tokens like [CLS] and [SEP] as required by BERT. return_tensors='pt' ensures the output is a PyTorch tensor.

## For a following pair
embeddings_following = tokenizer(sentence_A, sentence_B, return_tensors='pt')

## For a non-following pair
embeddings_not_following = tokenizer(sentence_A, sentence_C, return_tensors='pt')

5. Compute Logits and Apply Softmax to Get Probabilities

Pass the tokenized embeddings to the loaded BERTje model. The model outputs logits, which are then converted into probabilities using the softmax function.

## Predict for the following pair
with torch.no_grad(): # Disable gradient calculation for inference
    logits_following = model(**embeddings_following)[0]
probs_following = softmax(logits_following, dim=1)

## Predict for the non-following pair
with torch.no_grad():
    logits_not_following = model(**embeddings_not_following)[0]
probs_not_following = softmax(logits_not_following, dim=1)

6. Interpretation of Results

The output probs is a tensor with two values, representing the probabilities for the two possible classes:

• Index 0: Probability that sentence_B logically follows sentence_A (the "isNext" class).

• Index 1: Probability that sentence_B does not logically follow sentence_A (the "notNext" class).

A higher probability at index 0 indicates a stronger sequential relationship between the two sentences.

Example Output:

For sentence_A and sentence_B:

tensor([[0.8463, 0.1537]])

This indicates that there's approximately an 84.63% probability that "Een geweldige plek om te zijn." follows "Ik woon in Amsterdam."

For sentence_A and sentence_C:

tensor([[0.1205, 0.8795]])

This indicates that there's approximately an 87.95% probability that "De kat zat op de mat." does not follow "Ik woon in Amsterdam."

Summary

Utilizing BERTje for Next Sentence Prediction offers a powerful way to understand sentence relationships within Dutch text. The Hugging Face Transformers library makes its integration straightforward, enabling developers to incorporate this capability into various Natural Language Processing (NLP) tasks.

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Interview Questions

• What is the primary purpose of Next Sentence Prediction (NSP) in the pre-training of BERT models?

• How does BERTje specifically handle NSP for Dutch language texts?

• What are the two distinct output classes for NSP predictions with BERT-like models?

• Describe the process of tokenizing sentence pairs before they are fed into the BERTje model for NSP.

• What is the role of the softmax function in transforming raw model outputs (logits) into interpretable probabilities for NSP?

• Which specific Hugging Face transformers class is used to load a pre-trained model for the NSP task?

• What does a higher probability value at index 0 of the output tensor signify in the context of BERTje's NSP output?

• Is it possible to fine-tune BERTje on custom Dutch sentence-pair datasets to improve its NSP accuracy for specific domains?

• What types of datasets were instrumental in the pre-training of BERTje to enhance its sentence-level understanding capabilities?

• How might BERTje's performance on NSP for Dutch compare to that of multilingual BERT models when focusing on Dutch text?