Albert: A Lite Bert For Self
Successes in the field of language representation learning may be traced back to the introduction of the entire network pre-training. These pre-trained models have been beneficial for a wide variety of non-trivial NLP applications, including ones with limited training data.The improvement of machines at a reading comprehension task created for Chinese middle and high school English examinations is one of the most striking indicators of these advancements. These advancements show that having a sizable network is essential for optimal performance. Pre-training larger models and then distilling them to smaller ones for practical use is now standard procedure. Due to the importance of model size, the researchers inquire: Is having better NLP models as easy as having larger models?
The current hardware’s inability to store a sufficient amount of data presents a limitation to a satisfactory answer to this question. The state-of-the-art models we use today often include hundreds of millions or even billions of parameters, making it simple to run across these limitations when the researchers attempt to scale our models. Because the communication overhead is proportional to the number of parameters in the mode, distributed training may also drastically slow down training speed.
Reduction Techniques
Two of the main hurdles to scaling pre-trained models are eliminated through the parameter reduction techniques used by ALBERT.
Factorized Embedding Parameterization
Model Setup
A Complete Guide To Customer Acquisition For Startups
Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.
So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.
The problem with customer acquisition
As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.
To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.
So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.
How can you create the ideal customer acquisition strategy for your business?
- Define what your goals are
You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics â
All these metrics tell you how well you will be able to grow your business and revenue.
- Identify your ideal customers
- Choose your channels for customer acquisition
- Communicate with your customers
How Does Bert Operate
BERT operates using sequence-to-sequence models with the help of an encoder and decoder. The encoder enables it to turn input into embedding. On the other hand, the decoder helps in embedding string outputs.
BERT has a different structure than other languages. It stacks encoders with 12 base and 24 large encoders in total. BERT framework works by two modeling methods:
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The Contributions Of The Paper
- According to this study, bidirectional pretraining is critical for developing accurate language representations. BERT employs masked language models to allow for pre-trained deep bidirectional representations. It contrasts unidirectional language models for pretraining and a shallow concatenation of independently trained left-to-right and right-to-left LMs.
- The authors demonstrate that Pre-trained representations reduce the need for numerous heavily-engineered task-specific architectures. There are multiple task-specific architectures that BERT outperforms since it is the first representation model that uses fine tuning to attain the current best performance on a wide range of sentence and token level tasks. BERT has improved 11 NLP tasks. The code and pre-trained models are available at https://github.com/google-research/bert.
Transfer Learning From Supervised Data
The research demonstrates successful transfer from supervised tasks using large datasets, such as natural language inference and machine translation. Research in computer vision has also proved the value of transfer learning from large pre-trained models. One strategy that has proven to be beneficial is to fine-tune models that have been pre-trained using ImageNet.
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Distilbert A Distilled Version Of Bert: Smallerfaster Cheaper And Lighter
Through this paper, researchers offer a way to pre-train a compact general-purpose language representation model, DistilBERT, that can be fine-tuned to achieve excellent performance on a variety of applications. They use knowledge distillation during the pretraining phase to reduce the size of a BERT model by 40 percent while maintaining 97 percent of its language understanding capabilities and being 60 percent faster. The authors offer a triple loss that integrates language modeling, distillation, and cosine-distance losses to take use of the inductive biases learned by larger models during pretraining.
Knowledge Distillation
Knowledge distillation is a compression method employed in the distillation process. This approach involves training a smaller model, known as the student, to mimic the behavior of a larger model, known as the teacher, or an ensemble of models. The student is trained with a distillation loss over the soft target probabilities of the teacher:
where ti is a probability estimated by the teacher . This objective results in a rich training signal by leveraging the full teacherdistribution. Softmax-temperature is used for this purpose:
Where T determines how smooth the output distribution is and zi is the model score for the class i. During training, temperature T is kept the same for the student and the teacher however, during inference, T is set to 1 to recover a typical softmax.
DistilBERT: a distilled version of BERT
A Comprehensive Guide To Deep Q
For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAIâs Gym environment.
So, read on to know more.
What is Deep Q-Learning?
Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:
State> Next state> Action> Reward
The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.
Now, any understanding of Deep Q-Learning is incomplete without talking about Reinforcement Learning.
What is Reinforcement Learning?
For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.
What is Q-Learning Algorithm?
The 4 steps that are involved in Q-Learning:
Reference Links
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Comparison Between Elmo Gpt And Bert
In this section, we will compare BERT with previous language models, particularly ELMo and GPT. These three models are among the biggest advancements in NLP. ELMo is a bi-directional LSTM model and is able to capture context information from both directions. It’s a feature-based approach, which means the pre-trained representations are used as features. GPT and BERT are both transformer-based models. GPT only uses transformer decoders and is unidirectional. This means information only flows from the left to the right in GPT. In contrast, BERT only uses transformer encoders and is bidirectional. Therefore, it can capture more context information than GPT and tends to perform better when context information from both sides is important. GPT and BERT are fine-tuning-based approaches. Users can use the models on downstream tasks by simply fine-tuning model parameters.
Example Of Binary Classification With Bert
First, import the package and organize your data into two tsv files in the bert_data_path. In this introduction, I only use a training and a hold-out evaluation set, however, you can also use a test set . It is also a good practice to compare the training loss with a validation loss, and by defining a subset of the labeled data as a validation dataset the code can be modified simply, however, I do not do it here as once we do it training becomes even longer!
I use the bert-base-cased model in this example with the following parameters:
importtorch%matplotlib inline%load_ext autoreload%autoreload 2
The autoreload extension is already loaded. To reload it, use: %reload_ext autoreload
TASK_NAME="classification"params=","OUTPUT_DIR":f"outputs//","REPORTS_DIR":f'reports/_evaluation_report/',"MAX_SEQ_LENGTH":128,"TRAIN_BATCH_SIZE":32,"EVAL_BATCH_SIZE":32,"LEARNING_RATE":2e-5,"NUM_TRAIN_EPOCHS":6,"RANDOM_SEED":42,"GRADIENT_ACCUMULATION_STEPS":1,"WARMUP_PROPORTION":0.1,"OUTPUT_MODE":"classification","CONFIG_NAME":"config.json","CACHE_DIR":'cache/',"WEIGHTS_NAME":"pytorch_model.bin","DEVICE":torch.deviceelse"cpu")}
I use the batch sizes of 32 in training and evaluation, I train for 6 epochs and use a gradient batch size of 1. If you have a smaller memory, you can try to decrease the batch size to 16 but once you define smaller batchsizes than 16, the gradients might be very dependent on the chosen batch. In this case you might use gradient accumulation.
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Roberta: A Robustly Optimized Bert Pretraining Approach
In this paper, the authors report a replication study of BERT pretraining, which includes a thorough analysis of the impacts of hyperparameter tweaking and training set size. The paper shows that BERT was severely undertrained and provides RoBERTa, a more efficient recipe for training BERT models that can achieve performance on par with or better than any post-BERT technique. Our adjustments are straightforward and include the following:
Static vs. Dynamic Masking
BERT uses a system of random token masking and prediction.In the first BERT implementation, masking is performed only once during data preprocessing, resulting in a static mask. The authors evaluate this method against dynamic masking, in which a new masking pattern is generated each time a sequence is fed into the model. It is vital for larger datasets or for adding more steps to the pretraining process.
Model Input Format and Next Sentence Prediction
We next compare training without the NSP loss and training with blocks of text from a single document . We find that this setting outperforms the originally published BERTBASE results and that removing the NSP loss matches or slightly improves downstream task performance
Training with Large Batches
Text Encoding
Emotion Text Classification using RoBERTa
Now We Can Train Our Model
Finally, we can train the model according to the parameters defined in the params dictionary.Note that here I use no evaluation data but it is a good practice to do so, in order to not only see the training loss but also see the loss on another dataset that we do not use for training.
fromtorch.nnimportCrossEntropyLossfromtimeimporttimefromtqdmimporttqdm_notebook,trangenum_labels=2train_loss=global_step=0nb_tr_steps=0start=timemodel.trainloss_fct=CrossEntropyLossfor_intrange,desc="Epoch"):tr_loss=0nb_tr_examples,nb_tr_steps=0,0forstep,batchinenumerate):batch=tuplefortinbatch)input_ids,input_mask,segment_ids,label_ids=batchlogits=modelloss=loss_fct,label_ids.view)train_loss.appendifparams> 1:loss=loss/paramsloss.backwardprinttr_loss+=loss.itemnb_tr_examples+=input_ids.sizenb_tr_steps+=1if%params==0:optimizer.stepoptimizer.zero_gradglobal_step+=1end=time
Epoch: 0%| | 0/6
Epoch: 17%| | 1/6
Epoch: 33%| | 2/6
Epoch: 50%| | 3/6
Epoch: 67%| | 4/6
Epoch: 83%| | 5/6
Epoch: 100%|| 6/6
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Why Is Bert Unique In Relation To Gpt
There are two main ideas that made the BERT paper so highly successful. First, it built upon the idea put forward by GPT of pretraining a transformer model on a huge corpus of text and then fine-tuning it for specific NLP tasks. The second important idea is that it used a bidirectional transformer architecture stacking encoders from the original transformer on top of each other.
Unlike traditional models that were trained for specific language tasks both BERT and GPT pretrain their models semi-supervised on large text datasets such as Wikipedia or BooksCorpus with over 3 billion words in total. BERT was then fine-tuned on labelled datasets for NLP tasks such as sentiment analysis, question answering or named-entity recognition and well surpassed previous SOTA-results in many well-known benchmarks.
My Bear Is Brown Roosevelt Was The President Of The Us
Now you have an intuition of how BERT works: it is bidirectional as the pre-training is done by masking some words in the input sequence and by trying to train the model on two tasks:
1) predicting the masked words 2) predicting whether two sentence is actually following each other
So now lets go to the details.
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Pretraining Using Bert Fine
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingBERT, by Google AI Language2019 NAACL, Over 31000 Citations Language Model
- BERT, Bidirectional Encoder Representations from Transformers, is proposed, to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers.
- This pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.
This is a kind of self-supervised learning that pretext task is the language model learning while specific tasks are downstream tasks with fine-tuning.
Bert: Bidirectional Transformers For Language Understanding
One of the major advances in deep learning in 2018 has been the development of effective NLP transfer learning methods, such as ULMFiT, ELMo and BERT. The Transformer Bidirectional Encoder Representations aka BERT has shown strong empirical performance therefore BERT will certainly continue to be a core method in NLP for years to come.
Although the original article is not difficult read, it can be difficult for those without the necessary background to understand. This post will cover BERT, therefore a general idea and a short introduction to the method and the way to fine-tune the model on a binary classification task. I also show the relevant code but the exact code can be found here on github. This code is heavily based on the pytorch-transformers framework and is implemented in Pytorch.
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Advantages Of Applying Fine
- Leverages Transfer Learning: Pre-trained BERT already encodes a lot of semantics and syntactic information about the language. Hence, it takes less time to train the fine-tuned model.
- Need for Less Data: Using pre-trained BERT, we need very minimal task-specific fine-tuning and hence need less data for better performance for any of the NLP tasks.
Developments In Natural Language Processing Algorithms
Consider if you want to learn a new language, say Hindi and you know English very well.
The first thing is to understand the meaning of every word of the new language in the context of the known language. You will also understand synonyms and antonym of the language for a better vocabulary. This will help you to understand the semantic or meaning-related relationships. This is the basic concept used in Word2Vec and GloVe
The next step would be to translate simple and short sentences from English to Hindi. You would listen to each word in the English sentence, and then based on the training, you would translate each word by word from English to Hindi. This is the same concept used in Encoder and Decoder.
You can now translate short sentences, but to translate longer sentences, you need to pay attention to certain words in the sentence to understand the context better. This is done by adding an Attention mechanism to the Encoder-Decoder model. The attention mechanism allows you to pay attention to specific input words in the sentence to do a better job translating but still reading word by word in a sentence.
You are now good at translating and would like to increase the speed and accuracy of your translation. You need some sort of parallel processing as well as have an awareness of the context to understand long term dependencies. Transformersaddressed this requirement.
Letâs look at two sentences below
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Emotion Text Classification Using Bert
In the context of this tutorial, we shall go into some depth on implementing the BERT base model with regard to the classification of text. We will see how this cutting-edge Transformer model can accomplish incredibly high-performance metrics with respect to a large data set.
Bring this project to life
Commands to check for available GPU and RAM allocation on runtime
gpu_info = !nvidia-smigpu_info = '\n'.joinif gpu_info.find > = 0: print printelse: print
Install Required Libraries
- Transformer package from Hugging Face Library contains Pre-Trained Language models.
- ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras , and it was designed to assist in building, training, and deploying neural networks and other machine learning models.
!pip install ktrain!pip install transformers!pip install datasets
import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsimport ktrainfrom ktrain import textimport tensorflow as tffrom sklearn.model_selection import train_test_splitfrom datasets import list_datasetsfrom datasets import load_datasetfrom sklearn.metrics import classification_report, confusion_matriximport timeitimport warningspd.set_optionwarnings.simplefilter
Dataset Loading
## Train and validation dataemotion_t = load_datasetemotion_v = load_datasetprintprint## dataframeemotion_t_df = pd.DataFrameemotion_v_df = pd.DataFramelabel_names =
Train & Validation data Splitting
Instantiating a BERT Instance
Bidirectional Encoder Representations From Transformers: Bert
BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers.
- BERT has deep bidirectional representations meaning the model learns information from left to right and from right to left. The bidirectional models are very powerful compared to either a left-to-right model or the shallow concatenation of a left-to-right and a right-to-left model.
- BERT framework has two steps: pre-training and fine-tuning
- It is pre-trained from unlabeled data extracted from BooksCorpus and English Wikipedia
- BERT pre-trained model can be fine-tuned with just one additional output layer to solve multiple NLP tasks like Text Summarization, Sentiment Analysis, Question-Answer chatbots, Machine Translation, etc.
- A distinctive feature of BERT is its unified architecture across different tasks. There is a minimal difference between the pre-trained architecture and the architecture used for various down-stream tasks.
- BERT uses the Masked Language Model to use the left and the right context during pre-training to create a deep bidirectional Transformers.
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