id stringlengths 7 11 | source stringclasses 1
value | category stringclasses 11
values | title stringlengths 15 214 | text stringlengths 70 30.5k | url stringlengths 57 160 | tags stringlengths 3 113 | created_at int64 1.22B 1.79B |
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se_79321551 | stackoverflow | ARTIFICIAL_INTELLIGENCE | getting an error: object of type 'float' has no len() | getting an error: object of type 'float' has no len()
I am a naive in python and started learning python few months ago
I am working on twitter data in my local, it has 4 columns. ID, brand, sentiment, comment
def data_clean_pipeline(text):
#removing html tags
text = str(BeautifulSoup(text).get_text())... | https://stackoverflow.com/questions/79321551/getting-an-error-object-of-type-float-has-no-len | python, python-3.x, twitter, nlp, project | 1,735,732,880 |
se_75127920 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How do I combine lists in column of dataframe to a single list | How do I combine lists in column of dataframe to a single list
Some context, I have some data that I'm doing some text analysis on, I have just tokenized them and I want to combine all the lists in the dataframe column for some further processing.
My df is as:
df = pd.DataFrame({'title': ['issue regarding app', 'graph... | https://stackoverflow.com/questions/75127920/how-do-i-combine-lists-in-column-of-dataframe-to-a-single-list | python, pandas, list, dataframe, nlp | 1,673,811,691 |
se_79315936 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Is n-gram precision the number of elements in the intersection of one hypothesis and possibly many references? | Is n-gram precision the number of elements in the intersection of one hypothesis and possibly many references?
I was trying to understand how BLEU score works and noticed that if I had to compute the n-gram precisions and have multiple reference sentences, it makes more sense to turn everything into sets to remove dup... | https://stackoverflow.com/questions/79315936/is-n-gram-precision-the-number-of-elements-in-the-intersection-of-one-hypothesis | nlp, nltk, bleu | 1,735,489,512 |
se_35281004 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to make api.ai agent learn something dynamically? | How to make api.ai agent learn something dynamically?
I am currently using api.ai , to create agent to perform specific tasks, but one question i don't have answer to is , can i make it learn something while chatting , mean that i speak my name is 'John Cena' and she should store it and then whenever i ask her again b... | https://stackoverflow.com/questions/35281004/how-to-make-api-ai-agent-learn-something-dynamically | android, json, nlp, artificial-intelligence, dialogflow-es | 1,454,972,473 |
se_70778039 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Split each line in a file based on delimitters | Split each line in a file based on delimitters
This is the sample data in a file. I want to split each line in the file and add it to a dataframe. In some cases they have more than 1 child. So whenever they have more than one child new set of column have to be added child2 Name and DOB
(P322) Rashmika Chadda 15/05/199... | https://stackoverflow.com/questions/70778039/split-each-line-in-a-file-based-on-delimitters | python, split, nlp, delimiter | 1,642,628,131 |
se_79312133 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Getting all leaf words (reverse stemming) into one Python List | Getting all leaf words (reverse stemming) into one Python List
On the same lines as the solution provided in this link, I am trying to get all leaf words of one stem word. I am using the community-contributed (@Divyanshu Srivastava) package get_word_forms
Imagine I have a shorter sample word list as follows:
my_list =... | https://stackoverflow.com/questions/79312133/getting-all-leaf-words-reverse-stemming-into-one-python-list | python, nlp, nltk | 1,735,311,845 |
se_79274184 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to split and spelling correct arabic text without spaces into list of words | How to split and spelling correct arabic text without spaces into list of words
I'm looking for a way to split the Arabic text and correct the spelling. Whitespace is the first splitting criterion, then, Maybe based on a dictionary of correct words the splitting should done, considering spelling issues:
تشرابالقطط الح... | https://stackoverflow.com/questions/79274184/how-to-split-and-spelling-correct-arabic-text-without-spaces-into-list-of-words | algorithm, deep-learning, nlp | 1,733,988,889 |
se_69135038 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to label review having both positive and negative sentiment words | How to label review having both positive and negative sentiment words
I have used vader library for labeling of amazon's reviews but it doesn't handle these types of reviews "No problems with it and does job well. Using it for Apple TV and works great. I would buy again no problem". This is positive sentence but the c... | https://stackoverflow.com/questions/69135038/how-to-label-review-having-both-positive-and-negative-sentiment-words | python-3.x, nlp, sentiment-analysis, review, vader | 1,631,290,010 |
se_79302218 | stackoverflow | ARTIFICIAL_INTELLIGENCE | torch.nn.functional.softmax giving inaccurate softmax output | torch.nn.functional.softmax giving inaccurate softmax output
I am trying to implement masked self-attention from scratch but when calculating the softmax for the similarity scores I get odd results. I looked at the documentation and other questions posted on here but I still cant figure out what I am doing wrong. Belo... | https://stackoverflow.com/questions/79302218/torch-nn-functional-softmax-giving-inaccurate-softmax-output | python, pytorch, nlp, softmax | 1,734,931,097 |
se_79300733 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to apply semantic tokenize on sentence in java by NLP? | How to apply semantic tokenize on sentence in java by NLP?
Can an NLP model be used to tokenize a sentence based on its semantic meaning?
For example,
for the sentence: If the driver's age is more than 20,
the tokens would be:
Token1: if
Token2: driver age
Token3: more than
Token4: 20 | https://stackoverflow.com/questions/79300733/how-to-apply-semantic-tokenize-on-sentence-in-java-by-nlp | java, nlp, stanford-nlp | 1,734,859,237 |
se_79260748 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to install spacy? | How to install spacy?
I am using trying to install spacy library using 'pip install -U spacy' in the command prompt (run as admin) in Windows-11 O.S., but it shows some error I don't understand. I am using Python 3.13.0, gcc 13.2.0 and make 4.4.1. What could be the problem? Or is there any other way to install spacy?
... | https://stackoverflow.com/questions/79260748/how-to-install-spacy | python, pip, nlp, spacy | 1,733,580,474 |
se_56071689 | stackoverflow | ARTIFICIAL_INTELLIGENCE | What's the major difference between glove and word2vec? | What's the major difference between glove and word2vec?
What is the difference between word2vec and glove?
Are both the ways to train a word embedding? if yes then how can we use both? | https://stackoverflow.com/questions/56071689/whats-the-major-difference-between-glove-and-word2vec | machine-learning, nlp, word2vec, word-embedding, glove | 1,557,468,619 |
se_79298368 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Inspect all probabilities of BERTopic model | Inspect all probabilities of BERTopic model
Say I build a BERTopic model using
from bertopic import BERTopic
topic_model = BERTopic(n_gram_range=(1, 1), nr_topics=20)
topics, probs = topic_model.fit_transform(docs)
Inspecting probs gives me just a single value for each item in docs.
probs
array([0.51914467, 0. ... | https://stackoverflow.com/questions/79298368/inspect-all-probabilities-of-bertopic-model | python, nlp, topic-modeling | 1,734,727,774 |
se_52799472 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to make an AI bot of Natural Language Processing? | How to make an AI bot of Natural Language Processing?
I want to make AI bot which can understand only 4 words "Up", "Down", "Left", "Right".
as my friend make a python script which executes some task by the voice like to open youtube just say "Youtube" and Chrome browser will open with youtube.com URL. But the system ... | https://stackoverflow.com/questions/52799472/how-to-make-an-ai-bot-of-natural-language-processing | python, machine-learning, nlp, artificial-intelligence, opennlp | 1,539,491,401 |
se_35556433 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Any tutorial for developing spoken dialogue system in prolog? | Any tutorial for developing spoken dialogue system in prolog?
I would like to implement a human-machine spoken dialogue system in a context of bar, which takes as input a text phrase from the user (a request for a given product: chips, cola, water, coffee, etc.) and return as output the corresponding response (Beyond ... | https://stackoverflow.com/questions/35556433/any-tutorial-for-developing-spoken-dialogue-system-in-prolog | prolog, nlp, artificial-intelligence | 1,456,152,027 |
se_78585519 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Benepar for syntactic segmentation | Benepar for syntactic segmentation
I want to use Benepar with a French model to do a syntactic segmentation.
I followed the tutorial but I have always have this error
RuntimeError: Error(s) in loading state_dict for ChartParser:
Unexpected key(s) in state_dict: "pretrained_model.embeddings.position_ids".
I tried to ... | https://stackoverflow.com/questions/78585519/benepar-for-syntactic-segmentation | text, nlp, text-segmentation, french, benepar | 1,717,664,245 |
se_79293889 | stackoverflow | ARTIFICIAL_INTELLIGENCE | catelog sentences into 5 words that represent them | catelog sentences into 5 words that represent them
I have dataframe with 1000 text rows. df['text']
I also have 5 words that I want to know for each one of them how much they represnt the text (between 0 to 1)
every score will be in df["word1"] ,df["word2"] and etc
I will glad for recomendations how to do that
edit
r... | https://stackoverflow.com/questions/79293889/catelog-sentences-into-5-words-that-represent-them | python, pandas, nlp, text-mining, similarity | 1,734,603,407 |
se_79293919 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Determining most popular words in the English dictionary within a dictionary of words | Determining most popular words in the English dictionary within a dictionary of words
Forgive me if my wording is awful, but I'm trying to figure out how to determine the most used words in the English language from a set of words in a dictionary I've made. I've done some research on NLTK but can't seem to find a func... | https://stackoverflow.com/questions/79293919/determining-most-popular-words-in-the-english-dictionary-within-a-dictionary-of | python, nlp, nltk, detection | 1,734,603,844 |
se_71215965 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to save checkpoints for thie transformer gpt2 to continue training? | How to save checkpoints for thie transformer gpt2 to continue training?
I am retraining the GPT2 language model, and am following this blog :
https://towardsdatascience.com/train-gpt-2-in-your-own-language-fc6ad4d60171
Here, they have trained a network on GPT2, and I am trying to recreate a same. However, my dataset i... | https://stackoverflow.com/questions/71215965/how-to-save-checkpoints-for-thie-transformer-gpt2-to-continue-training | tensorflow, nlp, gpt-2 | 1,645,504,561 |
se_79287799 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to correctly identify entity types for tokens using spaCy using python? | How to correctly identify entity types for tokens using spaCy using python?
I'm using spaCy to extract and identify entity types (like ORG, GPE, DATE, etc.) from a text description. However, I am noticing some incorrect results, and I'm unsure how to fix this.
Here is the code I am using:
import spacy
nlp = spacy.loa... | https://stackoverflow.com/questions/79287799/how-to-correctly-identify-entity-types-for-tokens-using-spacy-using-python | python, machine-learning, nlp, spacy | 1,734,437,389 |
se_76238212 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Bert model splits words by its own | Bert model splits words by its own
I am tokenizing the input words using bert model.
The code is :
tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased',do_lower_case = False)
model = BertModel.from_pretrained("bert-base-multilingual-cased", add_pooling_layer=False, output_hidden_states=True, output... | https://stackoverflow.com/questions/76238212/bert-model-splits-words-by-its-own | python, nlp, huggingface-transformers, bert-language-model | 1,683,908,223 |
se_79279045 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Recommending a pre-train NER model for geospatial entities | Recommending a pre-train NER model for geospatial entities
I am trying to find the best pre-trained Hugging Face Transformer model exclusively dedicated to geospatial or location entities to extract location entities in English from a text. Does it work way better than roberta-large? | https://stackoverflow.com/questions/79279045/recommending-a-pre-train-ner-model-for-geospatial-entities | nlp, geolocation, pipeline, geospatial, huggingface-transformers | 1,734,108,820 |
se_79283846 | stackoverflow | ARTIFICIAL_INTELLIGENCE | --user-dir in Fairseq in failing | --user-dir in Fairseq in failing
I’m trying to fine-tune the IndicTrans2 model using fairseq-train, but I keep encountering the following error:
fairseq-train: error: argument --user-dir: invalid Optional value: 'C:/Users/sasid/Downloads/en-indic-exp/model_configs'
I’ve provided the --user-dir argument as the path to ... | https://stackoverflow.com/questions/79283846/user-dir-in-fairseq-in-failing | python, nlp, huggingface-transformers, large-language-model, fairseq | 1,734,332,595 |
se_77028326 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Searching for specific words in Corpus with R (tm package) | Searching for specific words in Corpus with R (tm package)
I have a Corpus (tm package), containing a collection of 1.300 different text documents [Content: documents: 1.300].
My goal is now to search the frequency of a specific wordlist in each of those documents. E.g. if my wordlist contains the words "january, feb... | https://stackoverflow.com/questions/77028326/searching-for-specific-words-in-corpus-with-r-tm-package | r, nlp, tm, corpus | 1,693,658,469 |
se_79057082 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Avoiding overlap in frequency and document frequency count in Quanteda | Avoiding overlap in frequency and document frequency count in Quanteda
Below is a dummy corpus of 4 documents.
The dictionary was developed to identify the frequency of words or phrases in the corpus, as well as the number of documents a word or phrases occurs in.
The world 'Australians' occurs in two dictionary keys ... | https://stackoverflow.com/questions/79057082/avoiding-overlap-in-frequency-and-document-frequency-count-in-quanteda | r, count, nlp, overlap, quanteda | 1,728,132,232 |
se_76780201 | stackoverflow | ARTIFICIAL_INTELLIGENCE | RuntimeError: "element 0 of tensors does not require grad and does not have a grad_fn" | RuntimeError: "element 0 of tensors does not require grad and does not have a grad_fn"
I am facing an issue while training a comment classification model using PyTorch Lightning with a pre-trained BERT model.
I encountered the following error during the training process:
RuntimeError: element 0 of tensors do... | https://stackoverflow.com/questions/76780201/runtimeerror-element-0-of-tensors-does-not-require-grad-and-does-not-have-a-gr | machine-learning, nlp, model, training-data, kaggle | 1,690,464,166 |
se_79264247 | stackoverflow | ARTIFICIAL_INTELLIGENCE | similarity from word to sentence after doing words Embedding | similarity from word to sentence after doing words Embedding
I have dataframe with 1000 text rows.
I did word2vec .
Now I want to create a new field which give me the distance from each sentence to the word that i want, lets say the word "king".
I thought about taking in each sentence the 4 closet words to the word ki... | https://stackoverflow.com/questions/79264247/similarity-from-word-to-sentence-after-doing-words-embedding | python, nlp, text-mining, word2vec, similarity | 1,733,732,044 |
se_75897180 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Langchain Chatbot with Memory + Vector Database | Langchain Chatbot with Memory + Vector Database
In Langchain, what is the suggested way to build a chatbot with memory and retrieval from a vector embedding database at the same time?
The examples in the docs add memory modules to chains that do not have a vector database. Related issue. | https://stackoverflow.com/questions/75897180/langchain-chatbot-with-memory-vector-database | nlp, chatbot, language-model, langchain | 1,680,256,745 |
se_78039417 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Questions about training LLMs on large text datasets for text generation from scratch | Questions about training LLMs on large text datasets for text generation from scratch
I made a fully custom made GPT in Jax (with Keras 3), using Tensorflow for the data pipeline.
I've trained the model on the Shakespeare dataset and got good results (so no problem with the model).
Now I want to train it on the Tiny-S... | https://stackoverflow.com/questions/78039417/questions-about-training-llms-on-large-text-datasets-for-text-generation-from-sc | python, tensorflow, deep-learning, nlp, tf.data.dataset | 1,708,590,835 |
se_74692176 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Huggingface: Fine-tuning (not enough values to unpack (expected 2, got 1)) | Huggingface: Fine-tuning (not enough values to unpack (expected 2, got 1))
I'm trying to fine-tune erfan226/persian-t5-paraphraser paraphrase generator model for Persian sentences. I used the Persian dataset of tapaco and reformatted it to match the glue (mrpc) dataset which is used in the fine-tuning documentation. I... | https://stackoverflow.com/questions/74692176/huggingface-fine-tuning-not-enough-values-to-unpack-expected-2-got-1 | nlp, huggingface, fine-tuning | 1,670,261,701 |
se_60142937 | stackoverflow | ARTIFICIAL_INTELLIGENCE | HuggingFace Transformers For Text Generation with CTRL with Google Colab's free GPU | HuggingFace Transformers For Text Generation with CTRL with Google Colab's free GPU
I wanted to test TextGeneration with CTRL using PyTorch-Transformers, before using it for fine-tuning. But it doesn't prompt anything like it does with GPT-2 and other similar language generation models. I'm very new for this and a... | https://stackoverflow.com/questions/60142937/huggingface-transformers-for-text-generation-with-ctrl-with-google-colabs-free | python, deep-learning, nlp, pytorch, huggingface-transformers | 1,581,297,261 |
se_70698947 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to import and use stopwords list from NLTK? | How to import and use stopwords list from NLTK?
I already imported stopwords from nltk.corpus, but I get STOPWORDS is not defined error. Below is my code:
import nltk
from nltk.corpus import stopwords
#Create stopword list:
stopwords = set(STOPWORDS)
The above gives the following error:
NameError: name 'STOPWORDS' is... | https://stackoverflow.com/questions/70698947/how-to-import-and-use-stopwords-list-from-nltk | python, machine-learning, nlp, nltk, stop-words | 1,642,087,128 |
se_65927060 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Fine-tune BERT for a specific domain on a different language? | Fine-tune BERT for a specific domain on a different language?
I want to fine-tune on a pre-trained BERT model.
However, my task uses data within a specific domain (say biomedical data).
Additionally, my data is also in a language different from English (say Dutch).
Now I could fine-tune the Dutch bert-base-dutch-cased... | https://stackoverflow.com/questions/65927060/fine-tune-bert-for-a-specific-domain-on-a-different-language | python-3.x, deep-learning, nlp, bert-language-model | 1,611,780,045 |
se_79253283 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Counting the Frequency of Some Words within some other Key Words in Text | Counting the Frequency of Some Words within some other Key Words in Text
I have two sets of word lists - first one I called search words and the second one I called key words. My goal is to calculate the frequency of search words within 10 words of key words. For example, assume that the word - acquire - is in key wor... | https://stackoverflow.com/questions/79253283/counting-the-frequency-of-some-words-within-some-other-key-words-in-text | python, pandas, nlp | 1,733,367,906 |
se_37646254 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Keyword/phrase extraction from free text using NLTK and Python for structured queries | Keyword/phrase extraction from free text using NLTK and Python for structured queries
I want to interpret specific keywords from a free text such as "I want to order boiled eggs and spinach soup from nearest restaurants' and use them to search content from my database.
For e.g i want to extract following specific keyw... | https://stackoverflow.com/questions/37646254/keyword-phrase-extraction-from-free-text-using-nltk-and-python-for-structured-qu | python, nlp, nltk, information-retrieval | 1,465,157,129 |
se_69718376 | stackoverflow | ARTIFICIAL_INTELLIGENCE | NLP - Specific Text Extraction | NLP - Specific Text Extraction
I have to identify the country name from a random text. I have the country list.
I am struggling to find a solution that can train the model on the country list and when I provide a random text to that model as an input, it identifies the country name as an output.
eg:-
"I live in India... | https://stackoverflow.com/questions/69718376/nlp-specific-text-extraction | nlp, text-extraction | 1,635,229,839 |
se_76495143 | stackoverflow | ARTIFICIAL_INTELLIGENCE | What does surface form mean in relation extraction? | What does surface form mean in relation extraction?
The term "entity surface form" is repeatedly mentioned in most of relation extraction papers. What does it mean?
For example, in the REBEL paper, the author mentions that "a relation is considered correct only if the head and tail entity surface forms are correctly e... | https://stackoverflow.com/questions/76495143/what-does-surface-form-mean-in-relation-extraction | machine-learning, nlp, relation-extraction | 1,686,986,112 |
se_77410116 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to decide correct NLP approach for a project | How to decide correct NLP approach for a project
I'm working on an NLP project. My task is to determine the category and Sentiment Score of Turkish Call Center conversations from the conversations themselves. We are using Python as our programming language. However, I'm stuck in the initial stages of the project among... | https://stackoverflow.com/questions/77410116/how-to-decide-correct-nlp-approach-for-a-project | machine-learning, text, nlp | 1,698,932,726 |
se_7248372 | stackoverflow | ARTIFICIAL_INTELLIGENCE | NLP software for classification of large datasets | NLP software for classification of large datasets
For years I've been using my own Bayesian-like methods to categorize new items from external sources based on a large and continually updated training dataset.
There are three types of categorization done for each item:
30 categories, where each item must belong to on... | https://stackoverflow.com/questions/7248372/nlp-software-for-classification-of-large-datasets | machine-learning, nlp | 1,314,730,810 |
se_79227390 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to extract specific entities from unstructured text | How to extract specific entities from unstructured text
Given a generic text sentence (in a specific context) how can I extract word/entities of interest belonging to a specific "category" using python and any NLP library?
For example given a step for a culinary recipe Add an onion to a bowl of carrots as input text, ... | https://stackoverflow.com/questions/79227390/how-to-extract-specific-entities-from-unstructured-text | python, machine-learning, nlp, nltk, spacy | 1,732,636,008 |
se_79256257 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Why does moving ML model initialization into a function prevent GPU OOM errors when del, gc.collect(), and torch.cuda.empty_cache() fail? | Why does moving ML model initialization into a function prevent GPU OOM errors when del, gc.collect(), and torch.cuda.empty_cache() fail?
for model_name in model_list:
model = LLM(model_name, trust_remote_code=True)
results = evaluate_model(model, task)
del model
gc.collect()
torch.cuda.empty_cache... | https://stackoverflow.com/questions/79256257/why-does-moving-ml-model-initialization-into-a-function-prevent-gpu-oom-errors-w | python-3.x, nlp, garbage-collection, huggingface-transformers, vllm | 1,733,433,542 |
se_77607529 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Output probabilities of tokens generated by Llama 2 using Transformers | Output probabilities of tokens generated by Llama 2 using Transformers
Given input tokens, LLMs output the tokens in their vocabulary that have the highest probability of coming after the input tokens.
I would like to print the probability of each token generated by the model in response to a prompt to see how confide... | https://stackoverflow.com/questions/77607529/output-probabilities-of-tokens-generated-by-llama-2-using-transformers | nlp, huggingface-transformers, large-language-model, llama | 1,701,791,429 |
se_79249400 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Installing SpaCy in Fedora 41 | Installing SpaCy in Fedora 41
I am getting into troubles as I try to install spaCy in a Fedora 41, AMD Ryzen machine. I got this:
Preparing metadata (pyproject.toml): finished with status 'error'
error: subprocess-exited-with-error
× Preparing metadata (pyproject.toml) did not run successfully.
│ e... | https://stackoverflow.com/questions/79249400/installing-spacy-in-fedora-41 | python, nlp, spacy, fedora | 1,733,266,113 |
se_70693229 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to get Bigram/Trigram of word from prelisted unigram from a document corpus / dataframe column | How to get Bigram/Trigram of word from prelisted unigram from a document corpus / dataframe column
I have a dataframe with text in one of its columns.
I have listed some predefined keywords which I need for analysis and words associated with it (and later make a wordcloud and counter of occurrences) to understand topi... | https://stackoverflow.com/questions/70693229/how-to-get-bigram-trigram-of-word-from-prelisted-unigram-from-a-document-corpus | python, nlp, nltk | 1,642,061,175 |
se_70727739 | stackoverflow | ARTIFICIAL_INTELLIGENCE | TfidfVectorizer seems to be giving incorrect results | TfidfVectorizer seems to be giving incorrect results
I have a list of length 7 (7 subjectes)
Each element in the list contains a long string of words.
Each element of the list can be viewed as a topic with a long sentence that sets it apart
I want to check which words make each topic unique (each element in the list)
... | https://stackoverflow.com/questions/70727739/tfidfvectorizer-seems-to-be-giving-incorrect-results | python, scikit-learn, nlp, tf-idf, tfidfvectorizer | 1,642,312,988 |
se_79247672 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Error in getting Captum text explanations for text classification | Error in getting Captum text explanations for text classification
I have the following code that I am using to identify the most influential words used to correctly predict the text in the test dataset
import pandas as pd
import torch
from torch.utils.data import DataLoader
from transformers import BertTokenizer, Bert... | https://stackoverflow.com/questions/79247672/error-in-getting-captum-text-explanations-for-text-classification | machine-learning, pytorch, nlp, huggingface-transformers, text-classification | 1,733,230,065 |
se_79247594 | stackoverflow | ARTIFICIAL_INTELLIGENCE | euclidian distance from word to sentence after doing Vectorizer | euclidian distance from word to sentence after doing Vectorizer
I have dataframe with 1000 text rows.
I did TfidfVectorizer.
Now I want to create a new field which give me the distance from each sentence to the word that i want, lets say the word "king". df['king']
I thought about taking in each sentence the 5 close... | https://stackoverflow.com/questions/79247594/euclidian-distance-from-word-to-sentence-after-doing-vectorizer | pandas, dataframe, nlp, text-classification, tf-idf | 1,733,228,705 |
se_79234004 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Llama-3.2-1B-Instruct generate inconsistent output | Llama-3.2-1B-Instruct generate inconsistent output
I want to use Llama-3.2-1B-Instruct model, and although I have set "temperature": 0.0, "top_p":0.0 and "top_k":0, it still generates inconsistent output. This is how my pipeline looks like:
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=to... | https://stackoverflow.com/questions/79234004/llama-3-2-1b-instruct-generate-inconsistent-output | python, nlp, huggingface-transformers, large-language-model | 1,732,798,957 |
se_61770847 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Split Sentences at Bullets and Numbering? | Split Sentences at Bullets and Numbering?
I am trying to input text into my word processor to be split into sentences first and then into words.
An example paragraph:
When the blow was repeated,together with an admonition in
childish sentences, he turned over upon his back, and held his paws in a peculiar manner.
1... | https://stackoverflow.com/questions/61770847/split-sentences-at-bullets-and-numbering | python, nlp, nltk, sentence | 1,589,361,781 |
se_69561535 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Split text into sentences by and/or | Split text into sentences by and/or
Text String:
text = ‘Turn left and take the door between stairs and elevator. Turn right to the corridor.’
Desire Output:
splitted_sentences= [‘turn left’, ‘take the door between stairs and elevator’, ‘turn right to the corridor’]
How can we split this text into sentences as shown... | https://stackoverflow.com/questions/69561535/split-text-into-sentences-by-and-or | python, text, split, nlp, sentence | 1,634,154,550 |
se_79178041 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Normalization of token embeddings in BERT encoder blocks | Normalization of token embeddings in BERT encoder blocks
Following the multi-headed attention layer in a BERT encoder block, is layer normalization done separately on the embedding of each token (i.e., one mean and variance per token embedding), or on the concatenated vector of all token embeddings (the same mean and ... | https://stackoverflow.com/questions/79178041/normalization-of-token-embeddings-in-bert-encoder-blocks | nlp, normalization, bert-language-model, attention-model | 1,731,335,431 |
se_79180131 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to parse a resume with few shot method using the specified models from HuggingFace and Langchain? | How to parse a resume with few shot method using the specified models from HuggingFace and Langchain?
Model selection confusion and some errors while trying to parse a resume with the following codes
Trying to do a few shot prompting with a google flan t5 base model
While Doing so I am getting an error
ERROR:Service.... | https://stackoverflow.com/questions/79180131/how-to-parse-a-resume-with-few-shot-method-using-the-specified-models-from-huggi | nlp, prompt, langchain, large-language-model, huggingface | 1,731,396,000 |
se_64109483 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to recognize if string is human name? | How to recognize if string is human name?
So I have some text data that's been messily parsed, and due to that I get names mixed in with the actual data. Is there any kind of package/library that helps identify whether a word is a name or not? (In this case, I would be assuming US/western/euro-centric names)
Otherwise... | https://stackoverflow.com/questions/64109483/how-to-recognize-if-string-is-human-name | python, nlp | 1,601,326,405 |
se_78895710 | stackoverflow | ARTIFICIAL_INTELLIGENCE | NER versus LLM to extract name, gender, role and company from text | NER versus LLM to extract name, gender, role and company from text
I need to extract the name, gender, job title and employer/company name from newspaper articles, running the process on local hardware (no Cloud allowed) due to copyright reasons.
I've been playing around with Llama 3.1 but I'm finding I don't get usea... | https://stackoverflow.com/questions/78895710/ner-versus-llm-to-extract-name-gender-role-and-company-from-text | nlp, large-language-model, named-entity-recognition | 1,724,225,953 |
se_77603219 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Get attention masks from HF pipelines | Get attention masks from HF pipelines
How should returned attention masks be accessed from the FeatureExtractionPipeline in Huggingface?
The code below takes an embedding model, distributes it and a huggingface dataset across 8 GPUs on a single node, and performs inference on the inputs. The code requires the attentio... | https://stackoverflow.com/questions/77603219/get-attention-masks-from-hf-pipelines | nlp, huggingface-transformers, huggingface, huggingface-tokenizers, accelerate | 1,701,735,642 |
se_79225465 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Montreal Forced Aligner(MFA) taking too much time(almost 18 days still going on) to train a 33 GB corpus | Montreal Forced Aligner(MFA) taking too much time(almost 18 days still going on) to train a 33 GB corpus
WE are using Montreal Forced Aligner (MFA) 3.x to train an acoustic model on a large dataset (~33GB of audio and transcripts in an Indian language). The training process takes an extremely long time(almost 18 days... | https://stackoverflow.com/questions/79225465/montreal-forced-alignermfa-taking-too-much-timealmost-18-days-still-going-on | python, nlp, speech-recognition, kaldi, phoneme | 1,732,600,419 |
se_79224081 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Encoder Decoder Transformer model generate a repetitive token as output in text summarization | Encoder Decoder Transformer model generate a repetitive token as output in text summarization
I implemented a transformer Encoder Decoder (Bert2Bert) for text summarization task. In train phase train loss decreases but in prediction phase it generate a repetitive token as output for example [2,2,2,2,.....]. what shoul... | https://stackoverflow.com/questions/79224081/encoder-decoder-transformer-model-generate-a-repetitive-token-as-output-in-text | deep-learning, nlp, huggingface-transformers, bert-language-model | 1,732,556,341 |
se_79213439 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to get domain of words using WordNet in R? | How to get domain of words using WordNet in R?
I am trying to use WordNet in R to determine the domain of a list of words (e.g., sports, biology) and represent the results in a dataframe. The goal is to create a dataframe where each domain is a column, and each cell contains 0 or 1 depending on whether the word is ass... | https://stackoverflow.com/questions/79213439/how-to-get-domain-of-words-using-wordnet-in-r | r, nlp, wordnet | 1,732,241,422 |
se_11962955 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Extracting data from natural language text | Extracting data from natural language text
I have a set of text newspaper ads, I'd like to extract information like the item being sold and its price. These ads don't follow any structured format. I have access to many thousands of these ads.
Where should I start on this project? Is there some library that would help ... | https://stackoverflow.com/questions/11962955/extracting-data-from-natural-language-text | machine-learning, nlp | 1,344,993,713 |
se_59409984 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Is there a way to extract information from contracts using ML with including contract files and targeted strings as inputs and outputs? | Is there a way to extract information from contracts using ML with including contract files and targeted strings as inputs and outputs?
I'm studying whether we could extract certain fields of information (e.g. contract parties, start and end dates) from contracts automatically.
I am wondering if those pieces of inform... | https://stackoverflow.com/questions/59409984/is-there-a-way-to-extract-information-from-contracts-using-ml-with-including-con | machine-learning, nlp, information-extraction | 1,576,759,123 |
se_7693470 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Strategy for parsing natural language descriptions into structured data | Strategy for parsing natural language descriptions into structured data
I have a set of requirements and I'm looking for the best Java-based strategy / algorthm / software to use. Basically, I want to take a set of recipe ingredients entered by real people in natural english and parse out the meta-data into a structu... | https://stackoverflow.com/questions/7693470/strategy-for-parsing-natural-language-descriptions-into-structured-data | java, nlp, dsl, text-parsing | 1,318,026,651 |
se_79202614 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Understanding byte-pair encoding tokenization for Greek characters | Understanding byte-pair encoding tokenization for Greek characters
I am trying to train a new tokenizer with Greek text to later add the new tokens into the Llama 3.1 tokenizer using
tokenizer.add_tokens(list(new_tokens)).
However, upon training the byte-pair encoding tokenizer on Greek and Spanish text, the result l... | https://stackoverflow.com/questions/79202614/understanding-byte-pair-encoding-tokenization-for-greek-characters | encoding, nlp, tokenize, byte-pair-encoding | 1,732,005,086 |
se_76050901 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Haystack: save InMemoryDocumentStore and load it in retriever later to save embedding generation time | Haystack: save InMemoryDocumentStore and load it in retriever later to save embedding generation time
I am using InMemory Document Store and an Embedding retriever for the Q/A pipeline.
from haystack.document_stores import InMemoryDocumentStore
document_store = InMemoryDocumentStore(embedding_dim =768,use_bm25=True)
... | https://stackoverflow.com/questions/76050901/haystack-save-inmemorydocumentstore-and-load-it-in-retriever-later-to-save-embe | python, nlp, haystack | 1,681,880,670 |
se_66919407 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Calculating words similarity score in python | Calculating words similarity score in python
I'm trying to calculate books similarity by comparing the topics lists.
Need to get similarity score from the 2 lists between 0-1.
Example:
book1_topics = ["god", "bible", "book", "holy", "religion", "Christian"]
book2_topics = ["god", "Christ", "idol", "Jesus"]
Tried usi... | https://stackoverflow.com/questions/66919407/calculating-words-similarity-score-in-python | python, nlp, wordnet, cosine-similarity, sentence-similarity | 1,617,366,808 |
se_79190601 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Pyspark sentiment analysis invalid output | Pyspark sentiment analysis invalid output
I am trying to perform sentiment analysis for a use case. Most of the time, it is giving correct results, but in some cases, even positive comments are being marked as negative. How can I fix my code to achieve better accuracy?
My code
from pyspark.sql.functions import udf, co... | https://stackoverflow.com/questions/79190601/pyspark-sentiment-analysis-invalid-output | pyspark, nlp, nltk, huggingface-transformers | 1,731,623,094 |
se_60990897 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Best way of using hugging face's Mask Filling for more than 1 masked token at a time | Best way of using hugging face's Mask Filling for more than 1 masked token at a time
I am able to use hugging face's mask filling pipeline to predict 1 masked token in a sentence using the below:
!pip install -q transformers
from __future__ import print_function
import ipywidgets as widgets
from transformers impo... | https://stackoverflow.com/questions/60990897/best-way-of-using-hugging-faces-mask-filling-for-more-than-1-masked-token-at-a | python, neural-network, nlp, huggingface-transformers | 1,585,828,203 |
se_76937361 | stackoverflow | ARTIFICIAL_INTELLIGENCE | When to set `add_special_tokens=False` in huggingface transformers tokenizer? | When to set `add_special_tokens=False` in huggingface transformers tokenizer?
this is the default way of setting tokenizer in the Hugging Face "transformers" library:
from transformers import BertForSequenceClassification,BertTokenizer
tokenizer=BertTokenizer.from_pretrained('ProsusAI/finbert')
tokens=tokenizer.encode... | https://stackoverflow.com/questions/76937361/when-to-set-add-special-tokens-false-in-huggingface-transformers-tokenizer | python, nlp, tokenize, huggingface-transformers | 1,692,486,428 |
se_78911175 | stackoverflow | ARTIFICIAL_INTELLIGENCE | A language model for machine translation between a low-resource language and Portuguese using Tensorflow | A language model for machine translation between a low-resource language and Portuguese using Tensorflow
I'm trying to train a language model for machine translation between a low-resource language and Portuguese using Tensorflow. unfortunately, I'm getting the following error:
PS C:\Users\myuser\PycharmProjects\teste... | https://stackoverflow.com/questions/78911175/a-language-model-for-machine-translation-between-a-low-resource-language-and-por | python, tensorflow, machine-learning, nlp | 1,724,587,615 |
se_79177228 | stackoverflow | ARTIFICIAL_INTELLIGENCE | DASK to_csv() problems due to memory | DASK to_csv() problems due to memory
I'm cleaning my text data and afterwards want to save it to csv. Defined cleaning functions work fine, but when to_csv() part comes, here come the problems as well.
Maybe someone have faced similar problem and has a trick to share with me how it would be possible to solve it? Maybe... | https://stackoverflow.com/questions/79177228/dask-to-csv-problems-due-to-memory | csv, text, nlp, export-to-csv, dask | 1,731,320,013 |
se_79176535 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Using sub-classes as anchors and classes as positives and negatives in a Siamese network with triplet loss? | Using sub-classes as anchors and classes as positives and negatives in a Siamese network with triplet loss?
I’m experimenting with a Siamese network using triplet loss to categorize sub-classes into broader classes. My setup differs from traditional triplet loss models: It involves using the sub-class as the anchor an... | https://stackoverflow.com/questions/79176535/using-sub-classes-as-anchors-and-classes-as-positives-and-negatives-in-a-siamese | nlp, distance, bert-language-model, semantics, triplet | 1,731,304,679 |
se_67283506 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Hugging face tokenizer cannot load files properly | Hugging face tokenizer cannot load files properly
I am trying to train a translation model from sratch using HuggingFace's BartModel architecture. I am using a ByteLevelBPETokenizer to tokenize things.
The issue that I am facing is that when I save the tokenizer after training it is not loaded properly even though it ... | https://stackoverflow.com/questions/67283506/hugging-face-tokenizer-cannot-load-files-properly | python, nlp, huggingface-tokenizers | 1,619,528,175 |
se_79173053 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to convert character indices to BERT token indices | How to convert character indices to BERT token indices
I am working with a question-answer dataset UCLNLP/adversarial_qa.
from datasets import load_dataset
ds = load_dataset("UCLNLP/adversarial_qa", "adversarialQA")
How do I map character-based answer indices to token-based indices after tokenizing the context and qu... | https://stackoverflow.com/questions/79173053/how-to-convert-character-indices-to-bert-token-indices | python, nlp, dataset, large-language-model, bert-language-model | 1,731,165,333 |
se_79165649 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Handling Multiple Entity Candidates in Short Texts for Entity Linking with SciSpacy | Handling Multiple Entity Candidates in Short Texts for Entity Linking with SciSpacy
I am working on linking short texts to entities in a biomedical knowledge graph (UMLS CUIs) using SciSpacy for a research project. The goal is to analyze the relationship between the linked entity and a separate predefined entity.
My c... | https://stackoverflow.com/questions/79165649/handling-multiple-entity-candidates-in-short-texts-for-entity-linking-with-scisp | nlp, data-science, spacy, named-entity-recognition, entity-linking | 1,730,969,534 |
se_79144325 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to Separate Text and Code in Python Strings? | How to Separate Text and Code in Python Strings?
I've encountered an issue in python. I have a string that contains both a message and code, and I need to separate them and pass each to different functions. An example:
text = """
Can you change sum operation to multiplication?
def process(a: int, b: int):
print(a... | https://stackoverflow.com/questions/79144325/how-to-separate-text-and-code-in-python-strings | python, regex, string, nlp, text-processing | 1,730,370,069 |
se_79159805 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How can I share a complex spaCy NLP model across multiple Python processes to minimize memory usage? | How can I share a complex spaCy NLP model across multiple Python processes to minimize memory usage?
I'm working on a multiprocessing python application where multiple processes need access to a large, pre-loaded spaCy NLP model (e.g., en_core_web_lg). Since the model is memory-intensive, I want to avoid loading it se... | https://stackoverflow.com/questions/79159805/how-can-i-share-a-complex-spacy-nlp-model-across-multiple-python-processes-to-mi | nlp, multiprocessing, python-multiprocessing, spacy | 1,730,821,773 |
se_78437749 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Error 404 while calling customised assistant with ChatGPT API in R | Error 404 while calling customised assistant with ChatGPT API in R
To analyse sentences, I've trained a ChatGPT assistant that I'm calling from an R function to classify sentences:
library(openai)
Sys.setenv(
OPENAI_API_KEY = 'XXXXXXXXXXXXXXXXXX'
)
score_sentence_CSM <- function(mySentence) {
myScore <- creat... | https://stackoverflow.com/questions/78437749/error-404-while-calling-customised-assistant-with-chatgpt-api-in-r | r, nlp, openai-api, chatgpt-api, assistant | 1,715,010,482 |
se_52631965 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Text Classification + NLP + Data-mining + Data Science: Should I do stop word removal and stemming before applying tf-idf? | Text Classification + NLP + Data-mining + Data Science: Should I do stop word removal and stemming before applying tf-idf?
I am working on a text classification problem. The problem is explained below:
I have a dataset of events which contains three columns - name of the event, description of the event, category of t... | https://stackoverflow.com/questions/52631965/text-classification-nlp-data-mining-data-science-should-i-do-stop-word-re | nlp, data-mining, data-science, text-classification, tf-idf | 1,538,585,194 |
se_79148979 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Finding Root Form of Verbs using Curiosity-AI/Catalyst | Finding Root Form of Verbs using Curiosity-AI/Catalyst
I'm trying to find the root form of a verb. I run text through the pipeline and can identify all tokens which match PartOfSpeech.VERB but I don't know how to continue from there.
This is what I have so far:
const string text = "The disastrous cat runs after the fa... | https://stackoverflow.com/questions/79148979/finding-root-form-of-verbs-using-curiosity-ai-catalyst | c#, nlp | 1,730,483,881 |
se_78758312 | stackoverflow | ARTIFICIAL_INTELLIGENCE | DSPy can't retrieve passage with text embeddings in ChromaDB | DSPy can't retrieve passage with text embeddings in ChromaDB
I am working on a RAG application using DSPy and ChromaDB for pdf files.
At first I fetched the text from the pdf and add it to the Chromadb as chunks. Also added the embeddings of the chunks. And tried to Retrieve the chunk that is related to the query ... | https://stackoverflow.com/questions/78758312/dspy-cant-retrieve-passage-with-text-embeddings-in-chromadb | machine-learning, nlp, large-language-model, chromadb, dspy | 1,721,203,410 |
se_39643391 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Good-Turing smoothing implementation problems | Good-Turing smoothing implementation problems
I want to implement the Good-Turing smoothing method which will improve my language model.
Let's start with the theory (for simplicity, consider the unigram model).
There is a corpus (e.g. a literary work or any huge text). Through it we are building a language model. Af... | https://stackoverflow.com/questions/39643391/good-turing-smoothing-implementation-problems | python, scipy, nlp, nltk, smoothing | 1,474,558,885 |
se_79145419 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Is it possible to get embeddings from NV-Embed using Candle? | Is it possible to get embeddings from NV-Embed using Candle?
What I want to do is a CLI program that outputs embeddings of an arbitrary input.
To do that, I want to do an inference with an embeddings model, and I chose NV-Embed-v2. My framework of choice is Candle, but I also looked at Mistral-RS.
Basically, what I'm ... | https://stackoverflow.com/questions/79145419/is-it-possible-to-get-embeddings-from-nv-embed-using-candle | machine-learning, rust, nlp | 1,730,390,149 |
se_79155290 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Dutch sentiment analysis RobBERTje outputs just positive/negative labels, netural label is missing | Dutch sentiment analysis RobBERTje outputs just positive/negative labels, netural label is missing
When I run Dutch sentiment analysis RobBERTje, it outputs just positive/negative labels, netural label is missing in the data.
https://huggingface.co/DTAI-KULeuven/robbert-v2-dutch-sentiment
There are obvious neutral sen... | https://stackoverflow.com/questions/79155290/dutch-sentiment-analysis-robbertje-outputs-just-positive-negative-labels-netura | python, nlp, bert-language-model, roberta-language-model | 1,730,720,195 |
se_76768226 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Target modules for applying PEFT / LoRA on different models | Target modules for applying PEFT / LoRA on different models
I am looking at a few different examples of using PEFT on different models. The LoraConfig object contains a target_modules array. In some examples, the target modules are ["query_key_value"], sometimes it is ["q", "v"], sometimes something else.
I don't quit... | https://stackoverflow.com/questions/76768226/target-modules-for-applying-peft-lora-on-different-models | nlp, huggingface-transformers, huggingface, fine-tuning, peft | 1,690,348,998 |
se_79148620 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Path_To_Connection Error when reading in Docx file to R with Officer and Docxtractor | Path_To_Connection Error when reading in Docx file to R with Officer and Docxtractor
I have several hundred documents from a legal database in ".docx" format. I am trying to do some NLP work on the docs, but can't seem to get past 0. Don't want to post the test doc because of copyright. I keep getting an xml2 path_to_... | https://stackoverflow.com/questions/79148620/path-to-connection-error-when-reading-in-docx-file-to-r-with-officer-and-docxtra | r, nlp, docx, officer, xml2 | 1,730,475,861 |
se_60937617 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to reconstruct text entities with Hugging Face's transformers pipelines without IOB tags? | How to reconstruct text entities with Hugging Face's transformers pipelines without IOB tags?
I've been looking to use Hugging Face's Pipelines for NER (named entity recognition). However, it is returning the entity labels in inside-outside-beginning (IOB) format but without the IOB labels. So I'm not able to map ... | https://stackoverflow.com/questions/60937617/how-to-reconstruct-text-entities-with-hugging-faces-transformers-pipelines-with | nlp, tokenize, transformer-model, named-entity-recognition, huggingface-transformers | 1,585,594,683 |
se_42003560 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to get training dataset of OpenNLP models? | How to get training dataset of OpenNLP models?
I am using the following models of OpenNLP:
en-parser-chunking.bin
en-ner-person.bin
en-ner-location.bin
en-ner-organization.bin
I want to append my data in the training dataset on which these models are trained. So please tell me from where I can get that raw dataset? | https://stackoverflow.com/questions/42003560/how-to-get-training-dataset-of-opennlp-models | machine-learning, nlp, text-mining, opennlp | 1,486,042,355 |
se_79136808 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Why is Keras pretrained BERT MaskedLM producing inconsistent predictions? | Why is Keras pretrained BERT MaskedLM producing inconsistent predictions?
I am trying to use keras-nlp with a pretrained masked BERT model to predict some tokens in a sequence. However the model produces inconsistent results. What could be wrong or am i misunderstanding something?
import keras
import keras_nlp
import... | https://stackoverflow.com/questions/79136808/why-is-keras-pretrained-bert-maskedlm-producing-inconsistent-predictions | python, keras, nlp, bert-language-model, keras-nlp | 1,730,198,185 |
se_77544604 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Importing deepspeech error: Module not found | Importing deepspeech error: Module not found
I am trying to install DeepSearch library so that I can use the pretrained model to build the Speech to Text Project.
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-8-a1bcd52700aa> in <cell line: 1>()
----> 1 import deepspeech
... | https://stackoverflow.com/questions/77544604/importing-deepspeech-error-module-not-found | python, deep-learning, nlp, mozilla-deepspeech | 1,700,844,483 |
se_79132332 | stackoverflow | ARTIFICIAL_INTELLIGENCE | KV caching for varying length texts | KV caching for varying length texts
I am trying to do some strucutured text extraction using some kv caching tricks. For this example I will use the following model and data:
model_name = "Qwen/Qwen2.5-0.5B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map=... | https://stackoverflow.com/questions/79132332/kv-caching-for-varying-length-texts | pytorch, nlp, huggingface-transformers, transformer-model | 1,730,098,675 |
se_79139698 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Clustering for grouping sentences and then caption the cluster with a short name | Clustering for grouping sentences and then caption the cluster with a short name
I have a series of text utterances in summary form (form of sentences). I am trying to perform clustering and group them with similarity in context (not in literal meaning) and report the clusters with the common group items. The below co... | https://stackoverflow.com/questions/79139698/clustering-for-grouping-sentences-and-then-caption-the-cluster-with-a-short-name | nlp, cluster-analysis, sentence-transformers | 1,730,264,303 |
se_46001910 | stackoverflow | ARTIFICIAL_INTELLIGENCE | What's the best way to compare several corpora in natural language? | What's the best way to compare several corpora in natural language?
I've been doing LDA topic models of narrative reports in natural language for a research project (using Gensim with python). I have several smallish corpora (from 1400 to 200 docs each – I know, that's tiny!) that I'd like to compare, but I don't ... | https://stackoverflow.com/questions/46001910/whats-the-best-way-to-compare-several-corpora-in-natural-language | python, nlp, nltk, lda, topic-modeling | 1,504,274,490 |
se_75417127 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Implementing tf-idf in wordclouds | Implementing tf-idf in wordclouds
I have some google reviews for some universities in a dataframe like below df_unis. The column uni_name contains the university names. I wish to create word clouds for each university separately but in such a way that words appear larger in the word cloud of a university if they not o... | https://stackoverflow.com/questions/75417127/implementing-tf-idf-in-wordclouds | nlp, text-mining, tf-idf, word-cloud | 1,676,072,585 |
se_77182311 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Question about data_collator throwing a key error in Hugging face | Question about data_collator throwing a key error in Hugging face
I am trying to use data_collator function in hugging face using this code:
datasets = dataset.train_test_split(test_size=0.1)
train_dataset = datasets["train"]
val_dataset = datasets["test"]
print(type(train_dataset))
def data_collator(data):
# Initia... | https://stackoverflow.com/questions/77182311/question-about-data-collator-throwing-a-key-error-in-hugging-face | python, pytorch, nlp, huggingface-transformers, huggingface-tokenizers | 1,695,750,166 |
se_79136628 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to Log Custom Metrics with Metadata in Hugging Face Trainer during Evaluation? | How to Log Custom Metrics with Metadata in Hugging Face Trainer during Evaluation?
I'm working on a sentence regression task using Hugging Face’s Trainer. Each sample consists of:
input_ids: The tokenized sentence.
labels: A numerical scalar target (for regression).
metadata: A categorical string field (e.g., project_... | https://stackoverflow.com/questions/79136628/how-to-log-custom-metrics-with-metadata-in-hugging-face-trainer-during-evaluatio | python, nlp, huggingface-transformers, huggingface, huggingface-trainer | 1,730,195,414 |
se_79120086 | stackoverflow | ARTIFICIAL_INTELLIGENCE | NLTK Conditional Frequency Distribution with Aggregation | NLTK Conditional Frequency Distribution with Aggregation
I'm trying to write an NLTK ConditionalFreqDist with some aggregated fields based on data from the fileid.
Basically, I have a corpus of documents with fileids consisting of a DATE-TITLE format, e.g., '2024-10-23 TitleName'.
Multiple fileids can share the same d... | https://stackoverflow.com/questions/79120086/nltk-conditional-frequency-distribution-with-aggregation | nlp, nltk, frequency-distribution | 1,729,730,095 |
se_79130588 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Geometric visulalization of Cosine Similarity | Geometric visulalization of Cosine Similarity
I have calculated the cosine similarity between two documents in a very basic way by using TF-IDF vectorization in Python.
But I want to visualize the documents as a vectorized graph in a 3D space. Like this:
Here's the code I used for calculating cosine similarity:
from ... | https://stackoverflow.com/questions/79130588/geometric-visulalization-of-cosine-similarity | python, nlp, tf-idf, cosine-similarity, tfidfvectorizer | 1,730,032,352 |
se_79132073 | stackoverflow | ARTIFICIAL_INTELLIGENCE | word2vec download taking too long | word2vec download taking too long
Windows10, IDLE, The download time is too long for the below code.
import gensim.downloader as api
word_vec_list = api.load('word2vec-google-news-300')
Is it possible that I download only fewer words?
Is it possible to download the dataset as csv and use it later in the program? | https://stackoverflow.com/questions/79132073/word2vec-download-taking-too-long | nlp, gensim, word2vec | 1,730,090,317 |
se_59330597 | stackoverflow | ARTIFICIAL_INTELLIGENCE | How to obtain BERT sentence embeddings vector? | How to obtain BERT sentence embeddings vector?
I'm using the module bert-for-tf2 in order to wrap BERT model as Keras layer in Tensorflow 2.0 I've followed your guide for implementing BERT model as Keras layer.
I'm trying to extract embeddings from a sentence; in my case, the sentence is "Hello"
I have a question abou... | https://stackoverflow.com/questions/59330597/how-to-obtain-bert-sentence-embeddings-vector | keras, nlp, embedding, word-embedding, sentence | 1,576,276,314 |
se_58168936 | stackoverflow | ARTIFICIAL_INTELLIGENCE | Bert sentence embeddings | Bert sentence embeddings
Im trying to obtain sentence embeddings for Bert but Im not quite sure if Im doing it properly... and yes Im aware that exist such tools already such as bert-as-service but I want to do it myself and understand how it works.
Lets say I want to extract a sentence embedding from word embeddings... | https://stackoverflow.com/questions/58168936/bert-sentence-embeddings | machine-learning, nlp, word-embedding | 1,569,850,158 |
se_78920095 | stackoverflow | ARTIFICIAL_INTELLIGENCE | cannot import name 'split_torch_state_dict_into_shards' from 'huggingface_hub' | cannot import name 'split_torch_state_dict_into_shards' from 'huggingface_hub'
I've been using LLAMA 2 for research for a few months now and I import as follows:
from transformers import AutoModelForCausalLM, AutoTokenizer
device = torch.device("cuda")
tokenizer = AutoTokenizer.from_pretrained("meta-ll... | https://stackoverflow.com/questions/78920095/cannot-import-name-split-torch-state-dict-into-shards-from-huggingface-hub | python, nlp, huggingface-transformers, transformer-model, llama | 1,724,779,242 |
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