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

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job title normalizer

is there any way to normalize job titles using ml or nlp? examples: raw title: UX/UI Engineers normalized title: Software Engineers raw title: UX/UI Designer normalized title: Graphic Designers ...
pycoder's user avatar
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Is it methodologically correct to use the data to be used for finetuning in the pretrain phase of the BERT model?

Let us assume the training of a BERT model. An initial pre-train is performed with a large data set A. Subsequently a finetuning is performed with a dataset B which is part of A, but now with labels ...
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Why the standard deviation of the BERT weight initialization is 0.02 by default

The purpose of weight initialization in the neural network is to keep the variance of calculation output in the layers to 1.0, and it depends on the calculations involved in the layers. Initializing ...
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The using of golden dataset in Augmented SBERT Training

I use the training strategy of Augmented SBERT (Domain-Transfer). In the code example they use the golden-dataset (STSb) for the training evaluator. Here two code snippets of the example of sentence-...
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Interpretation of Evaluation Values of Augmented SBERT Training with EmbeddingSimilarityEvaluator()

I train a BI-Encoder to get an Augmented SBERT and I get a final training result. How can I interpret the following output of the final training result? ...
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Explanation : Simpler models beat BERT base

I have been trying to train different models for a multi-class classification task of texts. My data set consists of rows of text and its label. The texts are short sentences. I tried the following ...
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Assign layers and weights in BERT

I print the weight names and shape of the BERT transformer. Now, I want to assign the printed weight to the layers in the transformers architecture: In the following, I can assign query, key and ...
Christian01's user avatar
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What happens when I set is_decoder to True in the bert API from huggingface?

Please help me understand the implications of initialising the bert model from huggingface with is_decoder parameter set to True ...
figs_and_nuts's user avatar
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How does Bert masked language modelling task make sense if half the time the next sentence is wrong context in the sequence passed through the encoder

Bert has two types of tasks that it uses to learn contextual word embeddings: Masked word prediction Next sentence prediction I have read the paper and even there the training details are a little ...
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F1 and Exact-Match (EM) Score in Extractive QA NLP

I have a question as to how the F1 should be calculated in NLP and whether the text normalization is optional or not. So I have been working on a project where we created a closed-domain extractive QA ...
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TFRobertaSequenceClassification for Address Normalization task

I have dataset with two column: one with faulty addresses, and other with correct addresses. I want to train a model such that, I can use it later for correcting all the incoming faulty addresses. I ...
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How can I avoid the irrelevant number of sentences in the result?

The nature of the data I have is not arranged; however, I'm trying to extract the appropriate sentences for each query as a sample for ground truth. Also, the most critical problem is that I use the ...
Begnnier's user avatar
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How to use Bertweet model for topic modeling

The problem is implementation of Bertweet in a topic-modeling project with understandable output like BERTopic, i want to use it on a relatively large (20k tweets) unlabelled dataset to segment it ...
Rossin's user avatar
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Training model using BERT

I have generated dataset using chat gpt. Dataset has 9000 data recodes. It's 6 class sentiment analysis. classes are 0,1,2,3,4,5 I used 3000 recodes for training, 1200 recods for validation and ...
Sandun Tharaka's user avatar
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Help understanding working of KeyBERT for keyphrase extraction

I'm fairly new to reading and understanding research papers, so I wanted to get a second opinion on whether my understanding of KeyBERT was correct. Here is a high level overview of my understanding ...
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Combining Textual, Categorical and Numerical data for Semantic Search using SentenceTransformers model

I'm building a food semantic search model and I want to use a pre-trained SentenceTransformers model with cosine similarity. I'm using Epicurious dataset for the corpus which consists of textual (&...
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How to deal with short text data using NLP models?

Now I want to use my own domain data to train NLP model like BERT. The following is the details of my data: data length distribution: over 70% of my data has the length shorter than 5 and the largest ...
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Adapting a BERT-based model from HuggingFace for NER (named entity recognition) and RE (relation extraction)?

Context: NER (named entity recognition) and RE (relation extraction) from sentences obtained from radiology reports (medical text). There is a BERT-based model from HuggingFace I would like to use for ...
Pablo Messina's user avatar
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Why does everyone use BERT in research instead of LLAMA or GPT or PaLM, etc?

It could be that I'm misunderstanding the problems space and the iterations of LLAMA, GPT, and PaLM are all based on BERT like many language models are, but every time I see a new paper in improving ...
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Classification errors on 'bert-base-uncased' text classifier

Disclaimer : This is a long question, please be patient. Thanks in advance I am using bert-base-uncased for text-classification. I have 11 classes, and the classification is happening alright for most ...
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BERT + tensorflow + deterministic

Im using BERT in tensorflow, but when I try to turn it deterministic I got the error: "When determinism is enabled, random ops must have a seed specified. [[{{node dropout/dropout/random_uniform/...
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How to analyze social media data to see its impact on a game's sales

I work for a console gaming giant. We forecasted the sales for a RPG game that was to be released few months back. But the actual sales was twice the forecast. This compelled the developers to ...
Ritik P. Nayak's user avatar
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building embeddings for Phrases from scratch

I have a datadet with many phrases which I would like to embed them from scratch. I dont want the cosine of the words in order to get a phrase embedding, this is because the phrases may appear in a ...
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Word embeddings

I m looking into word embedding and I would like to ask if I could train words or sentences in two layers. And if I wanted that one layer is more important, how could I calculate it? For example ...
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BERT is a supervised learning or semi-supervised learning?

I use 'bert-base-cased' pre-trained model for encoding a dataset of text that was labeled to labels 0, 1. Then the encoded dataset is trained using BERT model imported from Transformer library. Does ...
Balive13's user avatar
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Can bert uncased predict text classification on foreign data?

I am trying to do the fake news/real news classification and used a pre-trained bert uncased model as transfer learning and it gave a solid 81% accuracy. But the problem is while doing sanity checks, ...
Jasmin Wilson's user avatar
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Use unclassified texts to improve BERT and RNN GRUN token classification model

I have a training (gold) labelled dataset than consists of 10000 sentences. The task is to create a model that classifies correctly unseen data with B-I-O tags. I have used a BERT and a GRU RNN model. ...
Spyros Triantsfyllou's user avatar
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Building BERT tokenizer with custom data

I'm wondering if there is a way to train our own Bert tokenizer instead of using pre-trained tokenizer provided by huggingface?
Loius Leong's user avatar
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what is the difference between NSP and text prediction

In BERT, NSP (Next Sentence Prediction) is for predicting next sentence based on context and Text prediction task is also for predicting next word or phrases. So, both are for predicting next sentence ...
tovijayak's user avatar
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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 ...
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Why type of help can we get on stack Exchange: Datascience?

I have a text classification dataset. The aim is to predict the category of an article based on its title. I have about 100 categories and 10 thousands instances. I've tried models like RNN, LSTM. I'...
Seydou GORO's user avatar
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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 ...
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detect/generate possible tokens for the dataset (name,type/category,signatures)

I have a dataset in the following format: name, type, signature Eg1 : A, 2, abc123 Eg2 : A, 2, ab3 Eg3 : A, 2, addc1 If we need to train the following dataset ...
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Huggingface not saving model checkpoint

I am trying to train T5 model. This is how my training arguments look like: ...
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Bert model for document sentiment classification

I am trying to fine-tune a Bert model for sentiment analysis. Instead of one sentence, my inputs are documents (including several sentences) and I am not removing dots. I was wondering if is it okay ...
mansoor sh's user avatar
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106 views

Clustering with BERT. Why are my clusters overlapped? How to improve BERT embeddings?

I am trying to create BERT embeddings of text data, then use dimensionality reduction and cluster. I tried with some big datasets like amazon reviews and 20newsgroups, but whenever I created ...
William Smith's user avatar
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The embedding output of bert

I want to get the embedding matrix of the Bert model (the input before the first block layers) to feed it into another architecture. I really appreciate it if you help me with that. Thanks
mansoor sh's user avatar
1 vote
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942 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 ...
Kalsi's user avatar
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Below text-classification model gives accuracy of 0.77 only on one dataset and 0.99 on spam-ham dataset? What should I do to increase with my dataset?

...
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Unstable training of BERT binary sequence classification. Higher loss but lower gradients

I'm training a BERT sequence classifier on a custom dataset. When the training starts, the loss is at around ~0.4 in a few steps. I print the absolute sum of gradients for each layer/item in the model ...
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Creating Word Embeddings using BERT for Machine-Generated Text Data

I have a dataset of machine-generated sequences that are not natural language, but the order of the words in the sequence is important. I want to create word embeddings using BERT to capture the ...
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BERTopic: Is it okay to ignore the first two topics?

I used BERTopic to generate a topic model over a large dataset of texts. The result is very appealing and the modeled topics are mostly perfectly interpretable for a human, especially compared to ...
oberbus's user avatar
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1 answer
170 views

Doubt in ELMO, BERT, Word2Vec

I read an answer on Quora where a NLP Practioner stated that using ELMO and BERT embeddings as input to LSTM or some RNN will defeat the purpose of ELMo and BERT. I am not sure I agree with the above ...
NeverGiveUp's user avatar
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Fine-tune GPT on sketch data (stroke-3)

These past days I have started a personal project where I would like to build a model that, given an uncompleted sketch, it can finish it. I was planning on using some pretrained models that are ...
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Do transformers (e.g. BERT) have an unlimited input size?

There are various sources on the internet that claim that BERT has a fixed input size of 512 tokens (e.g. this, this, this, this ...). This magical number also appears in the BERT paper (Devlin et al. ...
Mew's user avatar
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How to effectively use CharBERT for text similarity?

I'm looking into CharBERT for an university project, and I noticed that it was finetuned on many tasks like sentiment analysis, NER, and so on. I tried to use it to do text similarity by using only ...
Gab's user avatar
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How can we integrate zero shot classification with a supervised classifier?

We have two sets of labels that are known and unknown to the supervised classifier (SC). We infer for a test example using the SC and a zero shot classifier (ZC). Let's assume, our inference datapoint ...
user3190883's user avatar
1 vote
2 answers
212 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 ...
E.K.'s user avatar
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1 answer
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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 ...
Swagat Mishra's user avatar
1 vote
1 answer
258 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 ...
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