According to the paper, BERT encoder is trained to predict next sentence and masked tokens. Does this mean during pretraining, additional linear layers are attached to BERT encoder in order to do these predictions? If so, are those public BERT models like bert-base-uncase shipped with those additional linear layers removed(in other words, only encoder is kept)?
I tried to read the original code, but couldn't understand it. Searched online and no information was found.
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I am using a pre-trained BERT sentence transformer model, as described here https://www.sbert.net/docs/training/overview.html , to get embeddings for sentences.
I want to fine-tune these pre-trained embeddings, and I am following the instructions in the tutorial i have linked above. According to the tutorial, you fine-tune the pre-trained model by feeding it sentence pairs and a label score that indicates the similarity score between two sentences in a pair. I understand this fine-tuning happens using the architecture shown in the image below:
Each sentence in a pair is encoded first using the BERT model, and then the "pooling" layer aggregates (usually by taking the average) the word embeddings produced by Bert layer to produce a single embedding for each sentence. The cosine similarity of the two sentence embeddings is computed in the final step and compared against the label score.
My question here is - which parameters are being optimized when fine-tuning the model using the given architecture? Is it fine-tuning only the parameters of the last layer in BERT model? This is not clear to me by looking at the code example shown in the tutorial for fine-tuning the model.
I am building an machine learning text classification model in R. I want to classify the sentence into more than one label if it falls into multiple categories.
e.g.: "The phone screen resolution is awesome and the battery life as well" - currently I am able to classify the sentence into either Battery or Phone feature category but I want it to be classified into both.
The output can be like:
It will be great if anyone can help me with ideas or methods to get the above result.
I would suggest training a binary classifier for each label.
With some algorithms - like logistic regression - all you can do is train every binary classifier independently.
There are also so-called multilabel algorithms - they train all binary classifiers at the same time, and extract the same features from data for every classifier. An example is a neural network with a sigmoid last layer. See "support multilabel" section in http://scikit-learn.org/stable/modules/multiclass.html for a list of multilabel algorithms.
Of course, a multilabel algorithm will not necessarily outperform logistic regression, you have to try and see what works best for your problem.
I have a made a rf model in R having six predictors and a response. The predictive model seems to be good enough but we also wanted to generate a response surface for this model.
attach(al_mf)
library(randomForest)
set.seed(1)
rfalloy=randomForest(Mf~.,data=al_mf,mtry=6,importance=TRUE)
rfalloy
rfpred=predict(rfalloy,al_mf$Mf)
rfpred
sse=sum((rfpred-mean(al_mf$Mf))^2)
sse
ssr=sum((rfpred-al_mf$Mf)^2)
ssr
Rsqaure=1-(ssr/(sse+ssr))
Rsqaure
importance(rfalloy)
At a general level, since you haven't provided too many specifics about exactly what you are looking for in your response surface, here are a few hopefully helpful starting points:
Have you taken a look at rsm? This documentation provides some good use cases for the package.
These in-class notes from a University of New Mexico stats lecture are full of code examples related to response surfaces. Just check out the table of contents and you'll probably find what you're looking for.
This StackOverflow post also provides an example using the rgl package.
I would like to fit an LSTM model using MXNET in R for the purpose of predicting a continuous response (i.e., regression) given several continuous predictors. However, the mx.lstm() function seems to be geared toward NLP as it requires arguments which don't seem applicable to a regression problem (such as those related to embedding).
Is MXNET capable of this sort of modeling and, if not, what is an example of an appropriate tool (preferably in R)? Are there any tutorials relevant to the problem I've described?
LSTM is used for working with temporal data: text, speech, time series. If you want to predict a continuous response, then I assume you want to do something similar to time series analysis.
If my assumption is correct, then, please, take a look here. It gives quite a good example on how to use MxNet with R for time series on CPU. The GPU version is also available here.
I am building a language model in R to predict a next word in the sentence based on the previous words. Currently my model is a simple ngram model with Kneser-Ney smoothing. It predicts next word by finding ngram with maximum probability (frequency) in the training set, where smoothing offers a way to interpolate lower order ngrams, which can be advantageous in the cases where higher order ngrams have low frequency and may not offer a reliable prediction. While this method works reasonably well, it 'fails in the cases where the n-gram cannot not capture the context. For example, "It is warm and sunny outside, let's go to the..." and "It is cold and raining outside, let's go to the..." will suggest the same prediction, because the context of weather is not captured in the last n-gram (assuming n<5).
I am looking into more advanced methods and I found text2vec package, which allows to map words into vector space where words with similar meaning are represented with similar (close) vectors. I have a feeling that this representation can be helpful for the next word prediction, but i cannot figure out how exactly to define the training task. My quesiton is if text2vec is the right tool to use for next word prediction and if yes, what is the suitable prediction algorithm that can be used for this task?
You can try char-rnn or word-rnn (google a little bit).
For character-level model R/mxnet implementation take a look to mxnet examples. Probably it is possible to extend this code to word-level model using text2vec GloVe embeddings.
If you will have any success, let us know (I mean text2vec or/and mxnet developers). I will be very interesting case for R community. I wanted to perform such model/experiment, but still haven't time for that.
There is one implemented solution as an complete example using word embeddings. In fact, the paper from Makarenkov et al. (2017) named Language Models with Pre-Trained (GloVe) Word Embeddings presents a step-by-step implementation of training a Language Model, using Recurrent Neural Network (RNN) and pre-trained GloVe word embeddings.
In the paper the authors provide the instructions to run de code:
1. Download pre-trained GloVe vectors.
2. Obtain a text to train the model on.
3. Open and adjust the LM_RNN_GloVe.py file parameters inside the main
function.
4. Run the following methods:
(a) tokenize_file_to_vectors(glove_vectors_file_name, file_2_tokenize_name,
tokenized_file_name)
(b) run_experiment(tokenized_file_name)
The code in Python is here https://github.com/vicmak/ProofSeer.
I also found that #Dmitriy Selivanov recently published a nice and friendly tutorial using its text2vec package which can be useful to address the problem from the R perspective. (It would be great if he could comment further).
Your intuition is right that word embedding vectors can be used to improve language models by incorporating long distance dependencies. The algorithm you are looking for is called RNNLM (recurrent neural network language model). http://www.rnnlm.org/