Questions tagged [bert]
BERT stands for Bidirectional Encoder Representations from Transformers and is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers
126
questions with no upvoted or accepted answers
7
votes
1
answer
9k
views
How is WordPiece tokenization helpful to effectively deal with rare words problem in NLP?
I have seen that NLP models such as BERT utilize WordPiece for tokenization. In WordPiece, we split the tokens like playing to play and ##ing. It is mentioned that it covers a wider spectrum of Out-Of-...
- 696
3
votes
0
answers
1k
views
Combining heterogeneous numerical and text features
We want to solve a regression problem of the form "given two objects $x$ and $y$, predict their score (think about it as a similarity) $w(x,y)$". We have 2 types of features:
For each ...
- 81
3
votes
0
answers
180
views
Medical NER for French language
I'm currently exploring the options to extract medical NER specifically for French language. I tried SpaCy's general French NER but it wasn't helpful to the cause (...
- 285
3
votes
2
answers
1k
views
Detecting grammatical errors with BERT
We fine-tuned BERT (bert-base-uncased) model with CoLA dataset for sentence classification task. The dataset is a mix of ...
- 285
3
votes
0
answers
58
views
Structuring a LSTM Layer
I'm trying to improve an NER Bert sequence tagger using LSTM layers in TensorFlow. I'm a bit unclear on the interface and how a LSTM layer should be set up.
Currently, I'm taking in 3-5 sentences and ...
- 41
2
votes
0
answers
1k
views
How many samples in dataset are required to fine-tune BERT for binary classification?
I'm trying to fine-tune a BERT-based model for a binary classification task (data is in English). The dataset I'm working with is quite small (~500 samples, out of which 80% are currently used for ...
- 21
2
votes
0
answers
35
views
Comparing the cosine similarities of the same word representations, from two separate models (vector spaces)
I am comparing the cosine similarities of word representations derived from a BERT model and also from a static Word2Vec model.
I understand that the vector spaces of the two models are inherently ...
- 21
2
votes
0
answers
389
views
How to add custom embeddings for bert
Pretrained Bert input has token embeddings, segment embeddings, position embeddings. But I would like to add some custom embeddings along with them and feed them to pretrained bert. How can I ...
- 239
2
votes
0
answers
161
views
Ways to cluster word senses with word embeddings
I'm trying to semantically cluster polysemous words or word with different meanings in a corpus for my class study and I want to do it by word embeddings but I have no Idea how to reach to the ...
- 21
2
votes
2
answers
545
views
How to get sentiment score for a word in a given dataset
I have a sentiment analysis dataset that is labeled in three categories: positive, negative, and neutral. I also have a list of words (mostly nouns), for which I want to calculate the sentiment value, ...
- 21
2
votes
1
answer
2k
views
HuggingFace Transformers is giving loss: nan - accuracy: 0.0000e+00
I am a HuggingFace Newbie and I am fine-tuning a BERT model (distilbert-base-cased) using the Transformers library but the training loss is not going down, instead ...
- 21
2
votes
0
answers
142
views
Imbalance classes in Named Entity Recognition
I am currently working on a NER problem which attempts to extract 2 entities - place-of-interest(POI) and street from an address string in the Indonesian language.
I used IndoBert (available here) and ...
- 121
2
votes
0
answers
45
views
Social media text analysis
I'm currently analyzing Korean social media text. The below are the steps of the analysis.
Collect/crawling text data from social media (e.g. Twitter, Facebook), which are related to specific topics.
...
- 33
2
votes
0
answers
194
views
Loss first decreases and then increases
I am using pre-trained xlnet-base-cased model and training it further on real vs fake news detection dataset. I noticed a trend in accuracy for first epoch. ...
- 21
2
votes
0
answers
258
views
Remove subwords from BERT output
I'm trying to build a multilingual WSD system with BERT on top as the embedding layer.
In order to have better performances, after BERT finishes its job (and performs Transfer Learning), I need to ...
- 121
2
votes
0
answers
470
views
Weight matrices in transformers
I am trying to understand the transformer architecture.
I am aware that the encoder/decoder contains multiple stacked self attention layers. Further each layer contains multiple heads. For example ...
- 161
2
votes
0
answers
651
views
BERT for non-textual sequence data
I'm working on a deep learning solution for classifying sequence data that isn't raw text but rather entities (which have already been extracted from the text). I am currently using word2vec-style ...
- 21
2
votes
0
answers
213
views
Can BERT embeddings be used to reproduce the original content of the text?
From what I understand, BERT provides contextualized embeddings that are not deterministic the way Word2Vec embeddings (i.e. the word "Queen" doesn't always produce the same vector, it'll be different ...
- 21
2
votes
1
answer
359
views
Predicting word from a set of words
My task is to predict relevant words based on a short description of an idea. for example "SQL is a domain-specific language used in programming and designed for managing data held in a relational ...
- 221
2
votes
1
answer
935
views
How to classify neutral sentiments using BERT
We can do text classification as positive and negative as mentioned in below notebook. But is there any way to classify neutral sentiment also?
https://colab.research.google.com/github/google-...
- 21
2
votes
0
answers
215
views
Paragraph Generator using BERT or GPT
I am trying to generate similar sentences, called paragraph generation. For example, what is the name of the eldest brother of ram? - For these paragraphs can be - who is the oldest brother of ram? , ...
- 894
1
vote
0
answers
47
views
I can't get good performance from BERT
I trained NLP models. This is a subset (200 instances) of my data set of 10,000 instances:This the link of the dataset on pastebin
I compare an LSTM model with a glove model and a BERT model. I ...
- 131
1
vote
0
answers
108
views
Otimization of similarity search for multiple embeddings by creating a weighted artificial embedding
I have embeddings of text created with a BERT model. A group of these embeddings should be used to find similar embeddings corresponding to this group. I know that you can use average or max (or ...
- 113
1
vote
2
answers
728
views
Fine-tuned MLM based RoBERTa not improving performance
We have lots of domain-specific data (200M+ data points, each document having ~100 to ~500 words) and we wanted to have a domain-specific LM.
We took some sample data points (2M+) & fine-tuned ...
- 11
1
vote
2
answers
162
views
Using BERT to extract a list of words and phrases from documents
I have a list of words and phrases (~3k items). What are my options to extract them from documents (~3M of job descriptions) with NLP? I do not have labeled data.
For example my list of words and ...
- 405
1
vote
1
answer
109
views
Does high number of output labels affect the performance of BERT and how to handle the class imbalance issue while doing multi text classification?
I am using BERT to do multiclass text classification. The number of output classes I have to predict from is: 116 and there is high degree of class imbalance that I see.
We have the following kind of ...
1
vote
1
answer
187
views
Is there any concern for a pretrained model to overfitting to a fine-tuning task that has overlapping pretraining and training data?
Let's say my language model is pretrained on a general text corpus, and I want to use it for some specific downstream task that has it's datasets also included in the general corpus, is there any ...
- 11
1
vote
1
answer
369
views
How does BERT work for Aspect-Based sentiment analysis?
I have recently used a package to perform Aspect-Based Sentiment Analysis (ABSA) through a BERT model.
Briefly, the model takes two inputs:
words that constitute the aspects
a sentence on which we ...
1
vote
1
answer
275
views
BERTopic Visualization
I new to topic modeling and I'm trying to use BERTopic inside of PyCharm. I'm struggling to ...
- 141
1
vote
1
answer
359
views
Bertopic with embedding: unable to use find_topic
I've used BERTopic with success for the following tasks: get topics, visualise (topics, barcharts, documents ...) and DTM (extended to get area plot with considerable success).
However, I am unable to ...
- 111
1
vote
0
answers
407
views
What are the inputs of encoder and decoder layers of transformer architecture?
In the paper (attention is all you need), it says "embeddings" are the input of the encoding layer. As I know embeddings are the numerical representation of words which is (for example) the ...
- 91
1
vote
0
answers
273
views
How does bert produce variable output shape?
Suppose if I provide a list of sentences:
['I like python',
'I am learning python', # longest sentence of length 4 tokens
'Python is simple']
Bert will produce ...
- 11
1
vote
0
answers
13
views
Can I use Bert on data subsets and get a compatible representation for the whole dataset?
I need to build an embedding for a massive amount of phrases. I want to use BERT (through the library https://www.sbert.net/).
Can I build a partial representation of the data, say encoding 1000 ...
1
vote
0
answers
102
views
Can pre-trained transformers (I.e., BERT) handle numerical/spatial data
I’m curious to know if pre-trained transformers could handle search queries that include numerical data or make references to spatial relationships.
Take an example dataset of a list of restaurants, ...
- 93
1
vote
0
answers
193
views
BERT each Word Embedding in Keras
How to use BERT to extract the embeddings of every word in a sentence.
Suppose I pass my corpus of sentences with different lengths to a BERT model , I want to be able to extract the embeddings of ...
- 118
1
vote
0
answers
90
views
Getting the keywords of text classification prediction in real time
I am using a BERT based text classification model to classify sentences/documents. My use case is like I need to highlight the words that are responsible for classification of a particular class. This ...
1
vote
0
answers
35
views
Where to start with ChatBots?
I want to start my journey into ChatBots and how I can create them. I have read some articles regarding the type of chatbots. Basically there are 2 types of chatbots, one is a rule based and the other ...
- 1,886
1
vote
0
answers
353
views
How to Fine Tune a BERT model for sentiment analysis to get the best f1 score
I am building a multi-class sentiment analysis BERT model that's optimized to give the best f1 score.
More specifically, I train each epoch by optimizing binary cross entropy per class, taking the ...
- 238
1
vote
0
answers
215
views
Using Sentence-Bert with other features in scikit-learn
I have a dataset where one feature is text and 4 more features. Sentence-Bert vectorizer transforms text data into tensors. I can use these sparse matrices directly with a machine learning classifier. ...
- 11
1
vote
0
answers
282
views
NER prections with distilbert transformer model
I am trying to extract 'agreement date' label from a corpus of legal contracts. In the train dataset, I used pytorch-transformer model to train.
...
- 56
1
vote
0
answers
343
views
Is it possible to fine-tune a (Spanish RoBERTa) model for a different task?
I'm doing sentiment analysis of Spanish tweets.
After reviewing some of the recent literature, I've seen that there's been a most recent effort to train a RoBERTa model exclusively on Spanish text. It ...
- 131
1
vote
0
answers
41
views
How to do Bert Finetuning of failure cases?
I have a large dataset(public available) of text that is labelled. However the test distribution (actual production setting of company) while similar is not from the same source and thus tends to fail ...
- 73
1
vote
0
answers
43
views
NLP-Problem, language model BERT?
Right now I am in the process of deciding on my masters thesis topic. Right now I and my professor are thinking about the possibility of having a large dataset of product requirements given in a ...
- 11
1
vote
1
answer
974
views
Embedding from Transformer-based model from paragraph or documnet (like Doc2Vec)
I have a set of data that contains the different lengths of sequences. On average the sequence length is 600. The dataset is like this:
...
1
vote
1
answer
268
views
BERT is running out of memory in forward pass for my dictionary
Running code from this answer, my BERT is running out for my 4k words dictionary.
I don't need to do anything with these words yet, just make embeddings for my data. So, using this exactly:
...
- 137
1
vote
0
answers
27
views
Extracting layer output from Classification model of SimpleTransformer
I have fine tuned a bert base model for text classification task. Now, I want to extract hidden layer output so as to combine this output with other features to train a random forest model. Problem ...
- 21
1
vote
1
answer
1k
views
Bert and SVM classification
I'm trying to understand the concepts in the title and how they fit into the task of binary classification. According to my understanding so far, you can encode text using various feature-extraction ...
- 111
1
vote
0
answers
522
views
How do I handle class imbalance for text data when using pretrained models like BERT?
I have a skewed dataset consisting of samples of the form:
Category 1 10000
Category 2 2000
Category 3 400
Category 4 300
Category 5 100
The dataset ...
- 11
1
vote
0
answers
214
views
NLP Bert model to to calculate text similarity, same sentence but not close similarity
Dear expert here:
I have a simple program to calculate text similarity. The program is copied from internet. Initially, I have a list of sentences or stored in db and fetched from db, then
I make the ...
- 183
1
vote
0
answers
154
views
How to apply pruning on a BERT model?
I have trained a BERT model using ktrain (tensorflow wrapper) to recognize emotion on text, it works but it suffers from really slow inference. That makes my model not suitable for a production ...
