Are transformers bidirectional? The encoder does not have self-attention masking. Therefore is designed not to have any dependency limitation:
the token representation obtained at one position depends on all the tokens in the input
. This is what makes the Transformer encoder bidirectional.
Why is BERT bidirectional?
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.
How is BERT different from transformer?
BERT is only an encoder, while the original transformer is composed of an encoder and decoder
. Given that BERT uses an encoder that is very similar to the original encoder of the transformer, we can say that BERT is a transformer-based model.
Why does BERT only use encoder?
BERT is a pretraining model to do the downstream tasks such as question answering, NLI and other language tasks. So
it just needs to encode the language representations so that it could be used for other tasks
. That’s why it consists only of encoder parts.
Is BERT an encoder?
The BERT Encoder block implements the base version of the BERT network
. It is composed of 12 successive transformer layers, each having 12 attention heads. The total number of parameters is 110 million. Every token in the input of the block is first embedded into a learned 768-long embedding vector.
Is ELMo bidirectional?
ELMo uses bidirectional language model (biLM)
which is pre-trained on a large text corpus, to learn both words (e.g., syntax and semantics) and linguistic context (i.e., to model polysemy).
Is Bart bidirectional?
BART is a denoising autoencoder that maps a corrupted document to the original document it was derived from. It is implemented as a sequence-to-sequence model with
a bidirectional encoder over corrupted text
and a left-to-right autoregressive decoder.
Is Roberta bidirectional?
A robustly optimized method for pretraining natural language processing (NLP) systems that
improves on Bidirectional Encoder Representations from Transformers
, or BERT, the self-supervised method released by Google in 2018.
What is CLS and SEP?
“CLS” is the reserved token to represent the start of sequence while “SEP” separate segment (or sentence)
. Those inputs are. Token embeddings: general word embeddings. In short, it uses vector to represent token (or word).
Is GPT 2 better than BERT?
They are the same in that they are both based on the transformer architecture, but they are fundamentally different in that
BERT has just the encoder blocks from the transformer, whilst GPT-2 has just the decoder blocks from the transformer
.
Is BERT Large better than BERT base?
BERT beats other models where BERT large performs better than BERT base
. These results also cements the claim that increasing the model size would lead to the improvement in results. Although the larger model performs better, fine tuning and training such a model is difficult and requires a lot of horse-power.
Is gpt an encoder or decoder?
GPT-2 does not require the encoder part of the original transformer architecture as it is
decoder-only
, and there are no encoder attention blocks, so the decoder is equivalent to the encoder, except for the MASKING in the multi-head attention block, the decoder is only allowed to glean information from the prior words …
What is the difference between transformer encoder and decoder?
The transformer uses an encoder-decoder architecture.
The encoder extracts features from an input sentence, and the decoder uses the features to produce an output sentence (translation)
. The encoder in the transformer consists of multiple encoder blocks.
How is BERT different from Word2Vec?
Word2Vec will generate the same single vector for the word bank for both the sentences. Whereas,
BERT will generate two different vectors for the word bank being used in two different contexts
. One vector will be similar to words like money, cash etc. The other vector would be similar to vectors like beach, coast etc.
Why is BERT so good?
For me, there are three main things that make BERT so great. Number 1:
pre-trained on a lot of data
. Number 2: accounts for a word’s context. Number 3: open-source.
What are transformers NLP?
What is a Transformer? The Transformer in NLP is
a novel architecture that aims to solve sequence-to-sequence tasks while handling long-range dependencies with ease
. It relies entirely on self-attention to compute representations of its input and output WITHOUT using sequence-aligned RNNs or convolution.
Is there anything better than BERT?
XLnet outperforms BERT on 20 tasks
, often by a large margin. The new model achieves state-of-the-art performance on 18 NLP tasks including question answering, natural language inference, sentiment analysis, and document ranking.
Does BERT use Transformers?
BERT, which stands for Bidirectional Encoder Representations from Transformers,
is based on Transformers
, a deep learning model in which every output element is connected to every input element, and the weightings between them are dynamically calculated based upon their connection.
Is GPT bidirectional?
Unlike BERT models,
GPT models are unidirectional
. The major advantage of GPT models is the sheer volume of data they were pretrained on: GPT-3, the third-generation GPT model, was trained on 175 billion parameters, about 10 times the size of previous models.
What is difference between BERT and Bart?
Bert vs.
As the BART authors write,
(BART) can be seen as generalizing Bert (due to the bidirectional encoder) and GPT2 (with the left to right decoder)
. Bert is pretrained to try to predict masked tokens, and uses the whole sequence to get enough info to make a good guess.
Is Bart a transformer?
BART is a denoising autoencoder for pretraining sequence-to-sequence models. It is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
It uses a standard Transformer-based neural machine translation architecture
.
What does BART stand for?
The
San Francisco Bay Area Rapid Transit District
(BART) is a heavy-rail public transit system that connects the San Francisco Peninsula with communities in the East Bay and South Bay.
Which is better BERT or RoBERTa?
2. RoBERTa stands for “Robustly Optimized BERT pre-training Approach”.
In many ways this is a better version of the BERT model
.
Is BERT unsupervised?
Unlike previous models,
BERT is a deeply bidirectional, unsupervised language representation
, pre-trained using only a plain text corpus.
Is DistilBERT faster than BERT?
DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than bert-base-uncased,
runs 60% faster
while preserving over 95% of BERT’s performances as measured on the GLUE language understanding benchmark.
Is BERT Seq2Seq?
Putting it all together. As you can see,
the Seq2Seq model is a combination of the BERT encoder and TransformerXL decoder
.
Does BERT need preprocessing?
Preprocessing is not needed when using pre-trained language representation models like BERT
. In particular, it uses all of the information in a sentence, even punctuation and stop-words, from a wide range of perspectives by leveraging a multi-head self attention mechanism.
[CLS]
is a special classification token and the last hidden state of BERT corresponding to this token (h
[ CLS ]
) is used for classification tasks. BERT uses Wordpiece embeddings input for tokens. Along with token embeddings, BERT uses positional embeddings and segment embeddings for each token.
Is ELMo better than BERT?
Truly Bidirectional
BERT is deeply bidirectional due to its novel masked language modeling technique.
ELMo on the other hand uses an concatenation of right-to-left and left-to-right LSTMs
and ULMFit uses a unidirectional LSTM. Having bidirectional context should, in theory, generate more accurate word representations.
Does GPT-3 have encoder?
GPT-3’s architecture consists of two main components: an encoder and a decoder
. The encoder takes as input the previous word in the sentence and produces a vector representation of it, which is then passed through an attention mechanism to produce the next word prediction.
Who is Jay Alammar?
Speaker: Jay Alammar
Jay is
a Partner at STV, a $500m tech venture capital fund
.
How long did it take to train BERT?
Training on a dataset this large takes a long time. BERT’s training was made possible thanks to the novel Transformer architecture and sped up by using TPUs (Tensor Processing Units – Google’s custom circuit built specifically for large ML models). —64 TPUs trained BERT over the course of
4 days
.
Who is hugging face?
Hugging Face,
a company that first built a chat app for bored teens provides open-source NLP technologies
, and last year, it raised $15 million to build a definitive NLP library. From its chat app to this day, Hugging Face has been able to swiftly develop language processing expertise.
What is CLS embedding?
The most straightforward sentence embedding model is
the [CLS] vector used to predict sentence-level context (i.e., BERT NSP, ALBERT SOP) during the pre-training
. The [CLS] token summarizes the information from other tokens via a self-attention mechanism that facilitates the intrinsic tasks of the pre-training.
What is a bidirectional model?
A Bidirectional LSTM, or biLSTM, is
a sequence processing model that consists of two LSTMs: one taking the input in a forward direction, and the other in a backwards direction
.
Is GPT 2 better than BERT?
They are the same in that they are both based on the transformer architecture, but they are fundamentally different in that
BERT has just the encoder blocks from the transformer, whilst GPT-2 has just the decoder blocks from the transformer
.
