funNLP | English sensitive words , language detection | Natural Language Processing library
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QUESTION
For a large scale text analysis problem, I have a data frame containing words that fall into different categories, and a data frame containing a column with strings and (empty) counting columns for each category. I now want to take each individual string, check which of the defined words appear, and count them within the appropriate category.
As a simplified example, given the two data frames below, i want to count how many of each animal type appear in the text cell.
df_texts <- tibble(
text=c("the ape and the fox", "the tortoise and the hare", "the owl and the the
grasshopper"),
mammals=NA,
reptiles=NA,
birds=NA,
insects=NA
)
df_animals <- tibble(animals=c("ape", "fox", "tortoise", "hare", "owl", "grasshopper"),
type=c("mammal", "mammal", "reptile", "mammal", "bird", "insect"))
So my desired result would be:
df_result <- tibble(
text=c("the ape and the fox", "the tortoise and the hare", "the owl and the the
grasshopper"),
mammals=c(2,1,0),
reptiles=c(0,1,0),
birds=c(0,0,1),
insects=c(0,0,1)
)
Is there a straightforward way to achieve this keyword-matching-and-counting that would be applicable to a much larger dataset?
Thanks in advance!
ANSWER
Answered 2022-Apr-14 at 13:32Here's a way do to it in the tidyverse. First look at whether strings in df_texts$text contain animals, then count them and sum by text and type.
library(tidyverse)
cbind(df_texts[, 1], sapply(df_animals$animals, grepl, df_texts$text)) %>%
pivot_longer(-text, names_to = "animals") %>%
left_join(df_animals) %>%
group_by(text, type) %>%
summarise(sum = sum(value)) %>%
pivot_wider(id_cols = text, names_from = type, values_from = sum)
text bird insect mammal reptile
1 "the ape and the fox" 0 0 2 0
2 "the owl and the the \n grasshopper" 1 0 0 0
3 "the tortoise and the hare" 0 0 1 1
To account for the several occurrences per text:
cbind(df_texts[, 1], t(sapply(df_texts$text, str_count, df_animals$animals, USE.NAMES = F))) %>%
setNames(c("text", df_animals$animals)) %>%
pivot_longer(-text, names_to = "animals") %>%
left_join(df_animals) %>%
group_by(text, type) %>%
summarise(sum = sum(value)) %>%
pivot_wider(id_cols = text, names_from = type, values_from = sum)
QUESTION
I'm trying to figure out why Apple's Natural Language API returns unexpected results.
What am I doing wrong? Is it a grammar issue?
I have the following four strings, and I want to extract each word's "stem form."
// text 1 has two "accredited" in a different order
let text1: String = "accredit accredited accrediting accredited accreditation accredits"
// text 2 has three "accredited" in different order
let text2: String = "accredit accredits accredited accrediting accredited accredited accreditation"
// text 3 has "accreditation"
let text3: String = "accreditation"
// text 4 has "accredited"
let text4: String = "accredited"
The issue is with the words accreditation and accredited.
The word accreditation never returned the stem. And accredited returns different results based on the words' order in the string, as shown in Text 1 and Text 2 in the attached image.
I've used the code from Apple's documentation
And here is the full code in SwiftUI:
import SwiftUI
import NaturalLanguage
struct ContentView: View {
// text 1 has two "accredited" in a different order
let text1: String = "accredit accredited accrediting accredited accreditation accredits"
// text 2 has three "accredited" in a different order
let text2: String = "accredit accredits accredited accrediting accredited accredited accreditation"
// text 3 has "accreditation"
let text3: String = "accreditation"
// text 4 has "accredited"
let text4: String = "accredited"
var body: some View {
ScrollView {
VStack {
Text("Text 1").bold()
tagText(text: text1, scheme: .lemma).padding(.bottom)
Text("Text 2").bold()
tagText(text: text2, scheme: .lemma).padding(.bottom)
Text("Text 3").bold()
tagText(text: text3, scheme: .lemma).padding(.bottom)
Text("Text 4").bold()
tagText(text: text4, scheme: .lemma).padding(.bottom)
}
}
}
// MARK: - tagText
func tagText(text: String, scheme: NLTagScheme) -> some View {
VStack {
ForEach(partsOfSpeechTagger(for: text, scheme: scheme)) { word in
Text(word.description)
}
}
}
// MARK: - partsOfSpeechTagger
func partsOfSpeechTagger(for text: String, scheme: NLTagScheme) -> [NLPTagResult] {
var listOfTaggedWords: [NLPTagResult] = []
let tagger = NLTagger(tagSchemes: [scheme])
tagger.string = text
let range = text.startIndex.. Bool {
lhs.id == rhs.id
}
func hash(into hasher: inout Hasher) {
hasher.combine(id)
}
// MARK: - Comparable requirements
static func <(lhs: NLPTagResult, rhs: NLPTagResult) -> Bool {
lhs.id.uuidString < rhs.id.uuidString
}
}
}
// MARK: - Previews
struct ContentView_Previews: PreviewProvider {
static var previews: some View {
ContentView()
}
}
Thanks for your help!
ANSWER
Answered 2022-Apr-01 at 20:30As for why the tagger doesn't find "accredit" from "accreditation", this is because the scheme .lemma finds the lemma of words, not actually the stems. See the difference between stem and lemma on Wikipedia.
The stem is the part of the word that never changes even when morphologically inflected; a lemma is the base form of the word. For example, from "produced", the lemma is "produce", but the stem is "produc-". This is because there are words such as production and producing In linguistic analysis, the stem is defined more generally as the analyzed base form from which all inflected forms can be formed.
The documentation uses the word "stem", but I do think that the lemma is what is intended here, and getting "accreditation" is the expected behaviour. See the Usage section of the Wikipedia article for "Word stem" for more info. The lemma is the dictionary form of a word, and "accreditation" has a dictionary entry, whereas something like "accredited" doesn't. Whatever you call these things, the point is that there are two distinct concepts, and the tagger gets you one of them, but you are expecting the other one.
As for why the order of the words matters, this is because the tagger tries to analyse your words as "natural language", rather than each one individually. Naturally, word order matters. If you use .lexicalClass, you'll see that it thinks the third word in text2 is an adjective, which explains why it doesn't think its dictionary form is "accredit", because adjectives don't conjugate like that. Note that accredited is an adjective in the dictionary. So "is it a grammar issue?" Exactly.
QUESTION
I am trying to clean up text using a pre-processing function. I want to remove all non-alpha characters such as punctuation and digits, but I would like to retain compound words that use a dash without splitting them (e.g. pre-tender, pre-construction).
def preprocess(text):
#remove punctuation
text = re.sub('\b[A-Za-z]+(?:-+[A-Za-z]+)+\b', '-', text)
text = re.sub('[^a-zA-Z]', ' ', text)
text = text.split()
text = " ".join(text)
return text
For instance, the original text:
"Attended pre-tender meetings"
should be split into
['attended', 'pre-tender', 'meeting']
rather than
['attended', 'pre', 'tender', 'meeting']
Any help would be appreciated!
ANSWER
Answered 2022-Mar-29 at 09:14To remove all non-alpha characters but - between letters, you can use
[\W\d_](? ASCII only equivalent:
[^A-Za-z](? See the regex demo. Details:
[\W\d_] - any non-letter(? - a negative lookbehind that fails the match if there is a letter and a - immediately to the left, and right after -, there is any letter (checked with the (?=[^\W\d_]) positive lookahead).
See the Python demo:
import re
def preprocess(text):
#remove all non-alpha characters but - between letters
text = re.sub(r'[\W\d_](? Attended pre-tender etc meetings
QUESTION
Looping over a list of bigrams to search for, I need to create a boolean field for each bigram according to whether or not it is present in a tokenized pandas series. And I'd appreciate an upvote if you think this is a good question!
List of bigrams:
bigrams = ['data science', 'computer science', 'bachelors degree']
Dataframe:
df = pd.DataFrame(data={'job_description': [['data', 'science', 'degree', 'expert'],
['computer', 'science', 'degree', 'masters'],
['bachelors', 'degree', 'computer', 'vision'],
['data', 'processing', 'science']]})
Desired Output:
job_description data science computer science bachelors degree
0 [data, science, degree, expert] True False False
1 [computer, science, degree, masters] False True False
2 [bachelors, degree, computer, vision] False False True
3 [data, bachelors, science] False False False
Criteria:
- Only exact matches should be replaced (for example, flagging for 'data science' should return True for 'data science' but False for 'science data' or 'data bachelors science')
- Each search term should get it's own field and be concatenated to the original df
What I've tried:
Failed: df = [x for x in df['job_description'] if x in bigrams]
Failed: df[bigrams] = [[any(w==term for w in lst) for term in bigrams] for lst in df['job_description']]
Failed: Could not adapt the approach here -> Match trigrams, bigrams, and unigrams to a text; if unigram or bigram a substring of already matched trigram, pass; python
Failed: Could not get this one to adapt, either -> Compare two bigrams lists and return the matching bigram
Failed: This method is very close, but couldn't adapt it to bigrams -> Create new boolean fields based on specific terms appearing in a tokenized pandas dataframe
Thanks for any help you can provide!
ANSWER
Answered 2022-Feb-16 at 20:28You could use a regex and extractall:
regex = '|'.join('(%s)' % b.replace(' ', r'\s+') for b in bigrams)
matches = (df['job_description'].apply(' '.join)
.str.extractall(regex).droplevel(1).notna()
.groupby(level=0).max()
)
matches.columns = bigrams
out = df.join(matches).fillna(False)
output:
job_description data science computer science bachelors degree
0 [data, science, degree, expert] True False False
1 [computer, science, degree, masters] False True False
2 [bachelors, degree, computer, vision] False False True
3 [data, processing, science] False False False
generated regex:
'(data\\s+science)|(computer\\s+science)|(bachelors\\s+degree)'
QUESTION
Goal: to run this Auto Labelling Notebook on AWS SageMaker Jupyter Labs.
Kernels tried: conda_pytorch_p36, conda_python3, conda_amazonei_mxnet_p27.
! pip install farm-haystack -q
# Install the latest master of Haystack
!pip install grpcio-tools==1.34.1 -q
!pip install git+https://github.com/deepset-ai/haystack.git -q
!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.03.tar.gz
!tar -xvf xpdf-tools-linux-4.03.tar.gz && sudo cp xpdf-tools-linux-4.03/bin64/pdftotext /usr/local/bin
!pip install git+https://github.com/deepset-ai/haystack.git -q
# Here are the imports we need
from haystack.document_stores.elasticsearch import ElasticsearchDocumentStore
from haystack.nodes import PreProcessor, TransformersDocumentClassifier, FARMReader, ElasticsearchRetriever
from haystack.schema import Document
from haystack.utils import convert_files_to_dicts, fetch_archive_from_http, print_answers
Traceback:
02/02/2022 10:36:29 - INFO - faiss.loader - Loading faiss with AVX2 support.
02/02/2022 10:36:29 - INFO - faiss.loader - Could not load library with AVX2 support due to:
ModuleNotFoundError("No module named 'faiss.swigfaiss_avx2'",)
02/02/2022 10:36:29 - INFO - faiss.loader - Loading faiss.
02/02/2022 10:36:29 - INFO - faiss.loader - Successfully loaded faiss.
02/02/2022 10:36:33 - INFO - farm.modeling.prediction_head - Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
in
1 # Here are the imports we need
----> 2 from haystack.document_stores.elasticsearch import ElasticsearchDocumentStore
3 from haystack.nodes import PreProcessor, TransformersDocumentClassifier, FARMReader, ElasticsearchRetriever
4 from haystack.schema import Document
5 from haystack.utils import convert_files_to_dicts, fetch_archive_from_http, print_answers
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/haystack/__init__.py in
3 import pandas as pd
4 from haystack.schema import Document, Label, MultiLabel, BaseComponent
----> 5 from haystack.finder import Finder
6 from haystack.pipeline import Pipeline
7
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/haystack/finder.py in
6 from collections import defaultdict
7
----> 8 from haystack.reader.base import BaseReader
9 from haystack.retriever.base import BaseRetriever
10 from haystack import MultiLabel
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/haystack/reader/__init__.py in
----> 1 from haystack.reader.farm import FARMReader
2 from haystack.reader.transformers import TransformersReader
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/haystack/reader/farm.py in
22
23 from haystack import Document
---> 24 from haystack.document_store.base import BaseDocumentStore
25 from haystack.reader.base import BaseReader
26
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/haystack/document_store/__init__.py in
2 from haystack.document_store.faiss import FAISSDocumentStore
3 from haystack.document_store.memory import InMemoryDocumentStore
----> 4 from haystack.document_store.milvus import MilvusDocumentStore
5 from haystack.document_store.sql import SQLDocumentStore
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/haystack/document_store/milvus.py in
5 import numpy as np
6
----> 7 from milvus import IndexType, MetricType, Milvus, Status
8 from scipy.special import expit
9 from tqdm import tqdm
ModuleNotFoundError: No module named 'milvus'
pip install milvus
import milvus
Traceback:
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
in
----> 1 import milvus
ModuleNotFoundError: No module named 'milvus'
ANSWER
Answered 2022-Feb-03 at 09:29I would recommend to downgrade your milvus version to a version before the 2.0 release just a week ago. Here is a discussion on that topic: https://github.com/deepset-ai/haystack/issues/2081
QUESTION
I have a dataset of tens of thousands of dialogues / conversations between a customer and customer support. These dialogues, which could be forum posts, or long-winded email conversations, have been hand-annotated to highlight the sentence containing the customers problem. For example:
Dear agent, I am writing to you because I have a very annoying problem with my washing machine. I bought it three weeks ago and was very happy with it. However, this morning the door does not lock properly. Please help
Dear customer.... etc
The highlighted sentence would be:
However, this morning the door does not lock properly.
- What approaches can I take to model this, so that in future I can automatically extract the customers problem? The domain of the datasets are broad, but within the hardware space, so it could be appliances, gadgets, machinery etc.
- What is this type of problem called? I thought this might be called "intent recognition", but most guides seem to refer to multiclass classification. The sentence either is or isn't the customers problem. I considered analysing each sentence and performing binary classification, but I'd like to explore options that take into account the context of the rest of the conversation if possible.
- What resources are available to research how to implement this in Python (using tensorflow or pytorch)
I found a model on HuggingFace which has been pre-trained with customer dialogues, and have read the research paper, so I was considering fine-tuning this as a starting point, but I only have experience with text (multiclass/multilabel) classification when it comes to transformers.
ANSWER
Answered 2022-Feb-07 at 10:21This type of problem where you want to extract the customer problem from the original text is called Extractive Summarization and this type of task is solved by Sequence2Sequence models.
The main reason for this type of model being called Sequence2Sequence is because the input and the output of this model would both be text.
I recommend you to use a transformers model called Pegasus which has been pre-trained to predict a masked text, but its main application is to be fine-tuned for text summarization (extractive or abstractive).
This Pegasus model is listed on Transformers library, which provides you with a simple but powerful way of fine-tuning transformers with custom datasets. I think this notebook will be extremely useful as guidance and for understanding how to fine-tune this Pegasus model.
QUESTION
My current data-frame is:
|articleID | keywords |
|:-------- |:------------------------------------------------------:|
0 |58b61d1d | ['Second Avenue (Manhattan, NY)'] |
1 |58b6393b | ['Crossword Puzzles'] |
2 |58b6556e | ['Workplace Hazards and Violations', 'Trump, Donald J']|
3 |58b657fa | ['Trump, Donald J', 'Speeches and Statements']. |
I want a data-frame similar to the following, where a column is added based on whether a Trump token, 'Trump, Donald J' is mentioned in the keywords and if so then it is assigned True :
|articleID | keywords | trumpMention |
|:-------- |:------------------------------------------------------:| ------------:|
0 |58b61d1d | ['Second Avenue (Manhattan, NY)'] | False |
1 |58b6393b | ['Crossword Puzzles'] | False |
2 |58b6556e | ['Workplace Hazards and Violations', 'Trump, Donald J']| True |
3 |58b657fa | ['Trump, Donald J', 'Speeches and Statements']. | True |
I have tried multiple ways using df functions. But cannot achieve my wanted results. Some of the ways I've tried are:
df['trumpMention'] = np.where(any(df['keywords']) == 'Trump, Donald J', True, False)
or
df['trumpMention'] = df['keywords'].apply(lambda x: any(token == 'Trump, Donald J') for token in x)
or
lst = ['Trump, Donald J']
df['trumpMention'] = df['keywords'].apply(lambda x: ([ True for token in x if any(token in lst)]))
Raw input:
df = pd.DataFrame({'articleID': ['58b61d1d', '58b6393b', '58b6556e', '58b657fa'],
'keywords': [['Second Avenue (Manhattan, NY)'],
['Crossword Puzzles'],
['Workplace Hazards and Violations', 'Trump, Donald J'],
['Trump, Donald J', 'Speeches and Statements']],
'trumpMention': [False, False, True, True]})
ANSWER
Answered 2022-Jan-06 at 12:13try
df["trumpMention"] = df["keywords"].apply(lambda x: "Trump, Donald J" in x)
QUESTION
I have several masked language models (mainly Bert, Roberta, Albert, Electra). I also have a dataset of sentences. How can I get the perplexity of each sentence?
From the huggingface documentation here they mentioned that perplexity "is not well defined for masked language models like BERT", though I still see people somehow calculate it.
For example in this SO question they calculated it using the function
def score(model, tokenizer, sentence, mask_token_id=103):
tensor_input = tokenizer.encode(sentence, return_tensors='pt')
repeat_input = tensor_input.repeat(tensor_input.size(-1)-2, 1)
mask = torch.ones(tensor_input.size(-1) - 1).diag(1)[:-2]
masked_input = repeat_input.masked_fill(mask == 1, 103)
labels = repeat_input.masked_fill( masked_input != 103, -100)
loss,_ = model(masked_input, masked_lm_labels=labels)
result = np.exp(loss.item())
return result
score(model, tokenizer, '我爱你') # returns 45.63794545581973
However, when I try to use the code I get TypeError: forward() got an unexpected keyword argument 'masked_lm_labels'.
I tried it with a couple of my models:
from transformers import pipeline, BertForMaskedLM, BertForMaskedLM, AutoTokenizer, RobertaForMaskedLM, AlbertForMaskedLM, ElectraForMaskedLM
import torch
1)
tokenizer = AutoTokenizer.from_pretrained("bioformers/bioformer-cased-v1.0")
model = BertForMaskedLM.from_pretrained("bioformers/bioformer-cased-v1.0")
2)
tokenizer = AutoTokenizer.from_pretrained("sultan/BioM-ELECTRA-Large-Generator")
model = ElectraForMaskedLM.from_pretrained("sultan/BioM-ELECTRA-Large-Generator")
This SO question also used the masked_lm_labels as an input and it seemed to work somehow.
ANSWER
Answered 2021-Dec-25 at 21:51There is a paper Masked Language Model Scoring that explores pseudo-perplexity from masked language models and shows that pseudo-perplexity, while not being theoretically well justified, still performs well for comparing "naturalness" of texts.
As for the code, your snippet is perfectly correct but for one detail: in recent implementations of Huggingface BERT, masked_lm_labels are renamed to simply labels, to make interfaces of various models more compatible. I have also replaced the hard-coded 103 with the generic tokenizer.mask_token_id. So the snippet below should work:
from transformers import AutoModelForMaskedLM, AutoTokenizer
import torch
import numpy as np
model_name = 'cointegrated/rubert-tiny'
model = AutoModelForMaskedLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def score(model, tokenizer, sentence):
tensor_input = tokenizer.encode(sentence, return_tensors='pt')
repeat_input = tensor_input.repeat(tensor_input.size(-1)-2, 1)
mask = torch.ones(tensor_input.size(-1) - 1).diag(1)[:-2]
masked_input = repeat_input.masked_fill(mask == 1, tokenizer.mask_token_id)
labels = repeat_input.masked_fill( masked_input != tokenizer.mask_token_id, -100)
with torch.inference_mode():
loss = model(masked_input, labels=labels).loss
return np.exp(loss.item())
print(score(sentence='London is the capital of Great Britain.', model=model, tokenizer=tokenizer))
# 4.541251105675365
print(score(sentence='London is the capital of South America.', model=model, tokenizer=tokenizer))
# 6.162017238332462
You can try this code in Google Colab by running this gist.
QUESTION
I am working on some sentence formation like this:
sentence = "PERSON is ADJECTIVE"
dictionary = {"PERSON": ["Alice", "Bob", "Carol"], "ADJECTIVE": ["cute", "intelligent"]}
I would now need all possible combinations to form this sentence from the dictionary, like:
Alice is cute
Alice is intelligent
Bob is cute
Bob is intelligent
Carol is cute
Carol is intelligent
The above use case was relatively simple, and it was done with the following code
dictionary = {"PERSON": ["Alice", "Bob", "Carol"], "ADJECTIVE": ["cute", "intelligent"]}
for i in dictionary["PERSON"]:
for j in dictionary["ADJECTIVE"]:
print(f"{i} is {j}")
But can we also make this scale up for longer sentences?
Example:
sentence = "PERSON is ADJECTIVE and is from COUNTRY"
dictionary = {"PERSON": ["Alice", "Bob", "Carol"], "ADJECTIVE": ["cute", "intelligent"], "COUNTRY": ["USA", "Japan", "China", "India"]}
This should again provide all possible combinations like:
Alice is cute and is from USA
Alice is intelligent and is from USA
.
.
.
.
Carol is intelligent and is from India
I tried to use https://www.pythonpool.com/python-permutations/ , but the sentence are all are mixed up - but how can we make a few words fixed, like in this example the words "and is from" is fixed
Essentially if any key in the dictionary is equal to the word in the string, then the word should be replaced by the list of values from the dictionary.
Any thoughts would be really helpful.
ANSWER
Answered 2021-Dec-12 at 17:53You can first replace the dictionary keys in sentence to {} so that you can easily format a string in loop. Then you can use itertools.product to create the Cartesian product of dictionary.values(), so you can simply loop over it to create your desired sentences.
from itertools import product
sentence = ' '.join([('{}' if w in dictionary else w) for w in sentence.split()])
mapped_sentences_generator = (sentence.format(*tple) for tple in product(*dictionary.values()))
for s in mapped_sentences_generator:
print(s)
Output:
Alice is cute and is from USA
Alice is cute and is from Japan
Alice is cute and is from China
Alice is cute and is from India
Alice is intelligent and is from USA
Alice is intelligent and is from Japan
Alice is intelligent and is from China
Alice is intelligent and is from India
Bob is cute and is from USA
Bob is cute and is from Japan
Bob is cute and is from China
Bob is cute and is from India
Bob is intelligent and is from USA
Bob is intelligent and is from Japan
Bob is intelligent and is from China
Bob is intelligent and is from India
Carol is cute and is from USA
Carol is cute and is from Japan
Carol is cute and is from China
Carol is cute and is from India
Carol is intelligent and is from USA
Carol is intelligent and is from Japan
Carol is intelligent and is from China
Carol is intelligent and is from India
Note that this works for Python >3.6 because it assumes the dictionary insertion order is maintained. For older Python, must use collections.OrderedDict rather than dict.
QUESTION
We can create a model from AutoModel(TFAutoModel) function:
from transformers import AutoModel
model = AutoModel.from_pretrained('distilbert-base-uncase')
In other hand, a model is created by AutoModelForSequenceClassification(TFAutoModelForSequenceClassification):
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification('distilbert-base-uncase')
As I know, both models use distilbert-base-uncase library to create models. From name of methods, the second class( AutoModelForSequenceClassification ) is created for Sequence Classification.
But what are really differences in 2 classes? And how to use them correctly?
(I searched in huggingface but it is not clear)
ANSWER
Answered 2021-Dec-05 at 09:07The difference between AutoModel and AutoModelForSequenceClassification model is that AutoModelForSequenceClassification has a classification head on top of the model outputs which can be easily trained with the base model
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