XLNet language model is a very useful NLP library for natural languages. Although the library written in Scala on top of Apache Spark, it is possible to run XLNet language model in Python with NumPy and matplotlib. In this article, we will look after various aspect of XLNet language model and its Python implementation.
This is a must-have for anyone working with NLP and text, so if you’ve been procrastinating on learning what it is, now’s the time to get started.
What Is A Language Model?
At its most basic level, a language model is a system for predicting the next word in a sequence of words.
The most commonly used language models are:
-Markov chain models, which rely on statistical probability estimates of what word will follow another word based on their surrounding context.
This is how Google Translate works when translating between two languages, and how some speech-to-text software works.
-Conditional random field models, which are more complicated and rely on assigning probabilities to each possible path forward based on transitions within the sentence.
This is an ML algorithm designed specifically for machine learning tasks rather than statistical modeling tasks. This also underlies how spam filters work, as they are trying to learn from historical data what words tend to come after others in spam emails in order to classify new emails into spam or not spam.
XLNet is a language model implemented in Apache Spark, which has achieved state-of-the-art results on several different NLP tasks, including coreference resolution, paraphrase detection and semantic role labeling.
Excited already? With just a few commands XLNet will help you build your models. In this document we provide an easy way to start your journey with XLNet by providing the step by step guide for implementing various components of our system.
So what is XLNet model :
XLNet is a language model. A language model is a statistical model used in computational linguistics, and it is often trained on large amounts of text.
What is the purpose of XLNet Model ? :
The purpose of a language model is to determine the probability of some word or sequence of words given the preceding context. This means that the language model can then be fed into a text generation algorithm to create realistic sentences.
- XLNet is a language model used to predict the next word in a sentence
- XLNet is an open-source neural network that has been trained on millions of sentences in different languages
- XLNet is the successor to word2vec, which was released by Google in 2014
- Google uses XLNet in its Translate app
- XLNet allows the user to pass in any text, and it will output a sentence-by-sentence breakdown of what it thinks the text means
- The neural network can be trained on any data set
What are the benefits of XLNet Model ? :
- XLNet is an improvement over other models in that it can handle a variety of different languages, from French to Japanese
- XLNet also does not require any special training. It is able to learn as it goes, which allows for greater flexibility and adaptability
What are some usecases of XLNet model ? :
- The XLNet language model is particularly useful when building chatbots or other natural language processing applications. This means that you can create a software program that can respond to a variety of different questions and commands, while still understanding the nuance of natural human speech
- The XLNet language model can also be beneficial when creating a digital assistant or other type of speech-based software program. By using this model, you can ensure that your program will be able to understand the questions and commands that humans may give your program, which will help ensure its overall usability
- While XLNet is incredibly versatile and flexible, it does have some drawbacks. For example, it is not as robust as other language models and may not be able to handle all questions thrown at it in the same way. Therefore, if you are looking for something more specific or detailed, you may want to look into other models instead.
XLNet in comparison with BERT (XLNet vs BERT) :
BERT is an open-source library for training and using language models (LM)
XLNet uses the same hyperparameters as BERT but can be trained using any of the common LM training algorithms (e.g., maximum likelihood, contrastive divergence, etc.)
The most important differences between XLNet and BERT are:
-XLNet has 2 layers of attention that have been tuned to improve perplexity and accuracy on the WikiText-103 dataset
-XLNet has a more advanced way of handling n-grams
-XLNet is much faster than BERT
Lets see XLNet Language Model implementation in Python :
#Installing and importing dependencies
!pip install torch
!pip install transformers
#Installing libraries
import torch
import transformers
from transformers import XLNetTokenizer, XLNetModel, AdamW, get_linear_schedule_with_warmup
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report, accuracy
from textwrap import wrap
from pylab import rcParams
from torch import optim
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torch.utils.data import TensorDataset,RandomSampler,SequentialSampler
import torch.nn.functional as tFn
from keras.preprocessing.sequence import pad_sequences
#Load and Process your data accordingly .
#padding and encoding data
from transformers import XLNetTokenizer
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
mytxt = "mydata"
encodings = tokenizer.encode_plus(mytxt, add_special_tokens=True, max_length=16, return_tensors='pt', return_token_type_ids=False, return_attention_mask=True, pad_to_max_length=True)
from keras.preprocessing.sequence import pad_sequences
inputmytxt = "Input text"
encodings = tokenizer.encode_plus(inputmytxt, add_special_tokens=True, max_length=16, return_tensors='pt', return_token_type_ids=False, return_attention_mask=True, pad_to_max_length=False
attention_mask = pad_sequences(encodings['attention_mask'], maxlen=512, dtype=torch.Tensor ,truncating="post",padding="post")
inputids = pad_sequences(encoding['inputids'], maxlen=MAX_LEN, dtype=torch.Tensor ,truncating="post",padding="post")
#loadpretrained model
from transformers import XLNetForSequenceClassification
model = XLNetForSequenceClassification.from_pretrained('xlnet-base-cased', num_labels = 2)
model = model.to(device)
#test and validate modelThe above is a sample of XLNet model implementation, for more visit here.
XLNet is capable of learning useful language representations and embed them into an auto-encoder, which then be applied to sequence-to-sequence problems.
With respect to language modeling, XLNet is a good choice and outperforms other leading models by a large margin. It’s also easy to implement and scale to big data.Sometimes what we want is not to do something ourselves but to have someone do it for us.
XLNet’s biggest advantage over the other models mentioned before is that it can be trained using for example the language modeling toolkit in TensorFlow. Hope you liked the article at MLDots.
