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Questions tagged [nlp]

Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve natural language understanding, that is, enabling computers to derive meaning from human or natural language input, and others involve natural language generation.

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Weird behaviour when using RobERTA for text classification

I have a dataset with around 70 classes and the dataset is largely balanced ~150 samples per class. I am finetuning RoBERTA-base for 4 epochs with a ...
user1274878's user avatar
1 vote
1 answer
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How to choose a loss function and how to calculate the loss for Text Generation in Generative AI?

For the classification problems, what loss functions can I choose ? For the translation problem how do I decide whether the translation is good and how to choose a loss function? And what about the ...
Qiulang's user avatar
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+100

Using UMAP on text data (euclidean distance on jaccard distance matrix)

I am checking the capabilities of the UMAP dimensionality reduction algorithm, I am not sure whether the approach I am using is valid and does not violate the rules/limitations of this algorithm. ...
rkabuk's user avatar
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6 views

How to get pmi score and count for NLTK everygrams / pmi and count all possible sizes of phrases in my text . Same as below for everygrams

...
98fly's user avatar
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1 vote
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Train a LLM to learn the entropy of the use case

I want to train a LLM (prefered Llama-2-13b) to learn the entropy of german texts - to be specific sports news. I use perplexity as training metric and want to check the training success after the ...
Christian01's user avatar
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7 views

Does Fine Tuning with Custom Label Build Upon the Capability of Zero Shot Classification or Does It Train from Scratch?

The task is to classify email text bodies into exclusive categories like feedback, complaint etc. I have a labelled dataset available having about 350 samples. I have tried the ...
Della's user avatar
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keyword extraction and link to multiple sources

I have to perform a tech watch over a very rich subject which spans from software to deep physical issues and also, from business news to scientific articles. Usually, I would have first retrieved the ...
deb2014's user avatar
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Problem when merge two probability distribution [Pointer Network]

I'm trying to re-implement Pointer Gen Net from this paper. Ya but you don't really need to read the paper. To sum up briefly, I have a vector probability distribution over vocabulary called p_vocab. ...
jupyter's user avatar
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1 answer
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Public Email Classification Dataset but not Spam vs Ham

Context Working to deliver a POC on automated email classification (in customer service context) to tag emails as related to feedback, complain, lost and found etc. The tags are not entirely exclusive,...
Della's user avatar
  • 335
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0 answers
19 views

Finding accuracy of model that uses different labels than ground truth

I have an nlp model that has ground truth labels and predicted labels (that belong to different group of classes). For example, the ground truth labels are [art, computer science, history] and ...
Vidushi Maheshwari's user avatar
4 votes
1 answer
1k views

How do I prompt GPT-4 to look at a PDF in Jupyter Notebook?

I am a beginner. I purchased tokens to use GPT-4 and finally figured out how to import the GPT-4 model into my Jupyter Notebook. ...
Mas's user avatar
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Multilabel Classification - Flat Binary Classifiers vs Hierarchical Binary Classifiers

Was researching on multi label classification to solve the problem of tagging news articles with topics and countries, where tags follow the syntax <topic>-<country>, and would like to ...
curious-24-7's user avatar
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12 views

Fine tuning a model for question-answering task without context on the dataset

I have a small dataset (contains around 2.5k question answer pairs) which I would like to train a T-5 base model on. The code examples that I have came across fine-tune with a dataset which has ...
Nabid Hasan's user avatar
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0 answers
6 views

Subsequence classification

Given multiple paragraphs, is it possible to classify an entire paragraph while taking into account the surrounding paragraphs? Paragraph1 Paragraph2 Paragraph3 ...
BPDev's user avatar
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0 answers
16 views

How is a causal language model correctly fine-tuned?

I want to fine-tune an SLM like Phi-2 through the huggingface API. I am in doubt how to achieve that, because I see two ways to do that and I am wondering which is the correct way. The task is just a ...
Marcel Braasch's user avatar
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6 views

Which model and embedding to use for portuguese chat with docs

I would like to have a model to read all my personal documents, meeting notes and things like that, all text based, and then be able to ask questions like: what was decided about the feature x? what ...
Kelly Goedert's user avatar
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19 views

Insights about W0rd2Vec

As per my knowledge, Word2Vec is belongs to non-contextual embedding technique. this have only semantic relationship between words. We can implement Word2Vec, either in CBoW or skip-gram model. but i ...
Tovlk's user avatar
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Is this how you would go about this NLP Project?

What do you think of these steps? And where can I find help with this project? I am in a business class and was assigned a data science problem. I was advised to seek out a coder at my school who can ...
Layla M's user avatar
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1 answer
19 views

Question about contextual embeddings?

How do BERT and RoBERTa generate contextual embeddings? The articles I've read keep saying that transformer encoders work bidirectionally. Because of self-attention, they can look at every token, ...
abcd's user avatar
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30 views

Stream response from custom RASA actions to the chatbot

I am using RASA PRO with CALM. I was thinking of using openai api within a custom action and stream the streaming response coming from openai to my chatbot. Openai is giving me streaming response and ...
Avatar's user avatar
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0 answers
32 views

Best practises for creating datasets for the purpose of finetuning LLMs

I am working on a problem for which no datasets exist. I have obtained several examples from this domain, and so far have been using them in Large Language Model (LLM) prompts(few shot learning) but I ...
Karl 17302's user avatar
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12 views

Implementing Fuzzy Matching and NLP for Transaction Classification

I’m a trainee at a fintech startup, and I’m working on a project that involves classifying transactions using Natural Language Processing (NLP) and fuzzy matching techniques. The main goal is to ...
RAN's user avatar
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8 views

Do LSTM, GRU and Transformer models with less layers and units perform better than larger models when classifying short text sequences?

I am working with a Kaggle dataset with short Twitter messages as text input. I made a copy here. When testing LSTMS, GRUs, bi-directional versions of the GRUs, and the Encoder layers of a Transformer ...
Joachim Rives's user avatar
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0 answers
8 views

Will hypermeters tuned on sampled dataset work for the whole dataset?

I'm doing multi-label classification on text data using BERT model. Since the dataset is huge, around 50 thousand rows, I was thinking to use stratify sampling on dataset to reduce it to around 2-4 ...
Shaurya Uniyal's user avatar
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0 answers
10 views

Commonly used metric in NLP literature to compare ranked weighted results with variable importance for top-k results

I have two different search engines that always return the same results but in different orders. The results consist of websites along with confidence scores, which range from 100 to 10,000. The ...
hanugm's user avatar
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4 votes
1 answer
98 views

LLMs for text generation

We know that AI is rapidly growing. do we have any large language models (LLMs) to process images, pdf documents directly (fine-tune approach) for text generation tasks?
Tovlk's user avatar
  • 43
0 votes
0 answers
25 views

Similarity Scores between SQL tables

I'm trying to figure out the best way to get started on a project. I have two separate databases, one is a "Template" db and the other is "Content" db. For each table in the ...
Marc J's user avatar
  • 1
0 votes
0 answers
9 views

Character-wise accuracy for image-to-text models

is it possible to enforce image-to-text models like ViT or a simple CNN+Transformer to achieve character-wise accuracy? Here's the context of my project: I am developing a model to extract some ...
CarlV's user avatar
  • 1
0 votes
1 answer
28 views

What's the purpose of using MLM when pretraining?

If BERT is a stack of transformer encoders, and the encoder already operates bidirectionally, understanding both left and right contexts and generating contextual embeddings, what is the purpose of ...
user avatar
0 votes
0 answers
11 views

can decoder only large language model be fine tuned to perform well at semantic similarity search?

BERT based models are Encoder only which are well suited for text classification, and Semantic Text similarity search (If fine-tuned via sBERT). I want to know whether decoder only models like Llama2, ...
haneulkim's user avatar
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0 answers
15 views

How to find LLM that is best at STS task?

I'm trying to find large language models that maps an embedding vector in proximity if they are semantically similar, in Korean. I tried looking at bunch of leaderboard such as MTEB_ko-ko STS, AI Hub ...
haneulkim's user avatar
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1 answer
31 views

How do transformer-based architectures generate contextual embeddings?

How do transformer-based architectures, such as Roberta, etc., generate contextual embeddings? The issue is, I haven't found any articles that explain this process.
user avatar
0 votes
0 answers
21 views

Approach for Multi-class Classification of texts

I'm trying to do a project where I have paragraphs and I need to classify them into multiple labels. The dataset is around 40k rows with labels. I understand there is no one right approach but should ...
Shaurya Uniyal's user avatar
0 votes
1 answer
49 views

Fine tuning or just feature extraction or both using Roberta?

I'm reading a program that use the pre-trained Roberta model (roberta-base). The code first extracts word embeddings from each caption in the batch, using the last hidden state of the Roberta model. ...
user avatar
0 votes
0 answers
13 views

what is the main difference between ROUGE and BLUE?

Both (ROUGE, BLUE) are useful to find the similarity between machine generated summary and reference summary. what is the main difference?
Tovlk's user avatar
  • 43
0 votes
0 answers
9 views

Reducing language bias for text classification, transformer model

I am working on a text classification model predicting classes for text. We have languages from many parts of the world and some of our classes are dominated by specific languages. The model we are ...
Carl Rynegardh's user avatar
1 vote
1 answer
34 views

How do I automate testing and comparison of the performance of models with different layer depths, layer types, and unit counts?

I am testing the effects of different layer counts/depths, unit counts, and layer types for natural language processing. I made a Kaggle notebook where I manually create different layers and then ...
Joachim Rives's user avatar
0 votes
1 answer
17 views

Could You Suggest Me Some Details of Realizing This LLM?

I mean this hypothetical LLM: https://twitter.com/RokoMijic/status/1663299142431432704 I'm trying to figure out how the neural network (let's abstract from the data) can be realized. I understand that:...
avpol's user avatar
  • 21
0 votes
0 answers
47 views

RAG - how to deal with numerical data

I have a car marker companies data . I am creating chunks for different car models in llama index and using vector store index and it is giving decent outputs when asked questions . It fails poorly ...
Pulkit Mehta's user avatar
1 vote
0 answers
38 views

Training Models Directly with Transformer Attention Weights: A Viable Strategy?

I'm currently using pre-trained transformers to extract embeddings for sequence analysis, which are then used in downstream tasks. My process involves using the extracted embeddings as features for ...
pparker's user avatar
  • 392
1 vote
1 answer
36 views

How can I use contextual embeddings with BERT for sentiment analysis/classification

I have a BERT model which I want to use for sentiment analysis/classification. E.g. I have some tweets that need to get a POSITIVE,NEGATIVE or NEUTRAL label. I can't understand how contextual ...
average_discrete_math_enjoyer's user avatar
1 vote
1 answer
44 views

Top_p parameter in langchain

I am trying to understand the top_p parameter in langchain (nucleus sampling) but I can't seem to grasp it. Based on this we sort the probabilities and select a ...
Labyrinthian's user avatar
1 vote
1 answer
32 views

Aside from trial and error, how do I select the number of layers and unit counts for LSTMS, GRUs, and Transformer units for text and time series?

When deciding on the number of units and layers for text processing or time-series prediction I rely heavily on trial and error. First, I look for a reference or paper on the topic such as the white ...
Joachim Rives's user avatar
0 votes
1 answer
35 views

How can I get the list of pretrained large language models?

Is there any place I can get the list of pre-trained large language models in a neat way? Despite the most common ones like gpt, BARD, llama2, which llm do you suggest that can be used for RAG and ...
heyula's user avatar
  • 37
0 votes
1 answer
32 views

What ML model is best suited for an intelligent search assistant?

I'm working on my thesis project, and want to make an intelligent search assistant that understands context and, of course, processes and repsonds in natural languaje. The data I want to train this ...
Bito's user avatar
  • 1
0 votes
0 answers
12 views

Resources on website summarization using LLMs

I am working on a problem where I have to summarize business websites. I have to generate a short 100 word summary of the primary function of a given website. I am familiar with langchain url ...
Vinay Varahabhotla's user avatar
1 vote
1 answer
96 views

What do special tokens used for in Roberta?

When I use this code: ...
user avatar
0 votes
0 answers
33 views

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
0 votes
1 answer
20 views

How to choose ideal pretrained model for fine-tuning?

I started to work with LLMs lately and want to know how people choose their pre-trained models in their fine-tuning tasks? What is the criteria to choose the base model and which factors affect?
heyula's user avatar
  • 37
0 votes
0 answers
30 views

Can I fine tune MedPaLM model

Is it possible to fine-tune MedPaLM and MedPaLM2; Google's llms trained using PaLM specialized for medical domain. Can we fine-tune these models further to get more specialized models?
heyula's user avatar
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