Topic Inference with Zeroshot models

Overview

zeroshot_topics

Table of Contents

Installation

zeroshot_topics is distributed on PyPI as a universal wheel and is available on Linux/macOS and Windows and supports Python 3.7+ and PyPy.

$ pip install zeroshot_topics

Usage

from zeroshot_topics import ZeroShotTopicFinder
zsmodel = ZeroShotTopicFinder()
text = """can you tell me anything else okay great tell me everything you know about George_Washington.
he was the first president he was well he I'm trying to well he fought in the Civil_War he was a general
in the Civil_War and chopped down his father's cherry tree when he was a little boy he that's it."""
zsmodel.find_topic(text)

License

zeroshot_topics is distributed under the terms of

You might also like...
This repo stores the codes for topic modeling on palliative care journals.

This repo stores the codes for topic modeling on palliative care journals. Data Preparation You first need to download the journal papers. bash 1_down

topic modeling on unstructured data in Space news articles retrieved from the Guardian (UK) newspaper using API
topic modeling on unstructured data in Space news articles retrieved from the Guardian (UK) newspaper using API

NLP Space News Topic Modeling Photos by nasa.gov (1, 2, 3, 4, 5) and extremetech.com Table of Contents Project Idea Data acquisition Primary data sour

Biterm Topic Model (BTM): modeling topics in short texts
Biterm Topic Model (BTM): modeling topics in short texts

Biterm Topic Model Bitermplus implements Biterm topic model for short texts introduced by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, and Xueqi Cheng. Actua

⚡ boost inference speed of T5 models by 5x & reduce the model size by 3x using fastT5.
⚡ boost inference speed of T5 models by 5x & reduce the model size by 3x using fastT5.

Reduce T5 model size by 3X and increase the inference speed up to 5X. Install Usage Details Functionalities Benchmarks Onnx model Quantized onnx model

Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS)

Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS) Yoonhyung Lee, Joongbo Shin, Kyomin Jung Abstract: Although early

Source code for AAAI20 "Generating Persona Consistent Dialogues by Exploiting Natural Language Inference".

Generating Persona Consistent Dialogues by Exploiting Natural Language Inference Source code for RCDG model in AAAI20 Generating Persona Consistent Di

LightSeq: A High-Performance Inference Library for Sequence Processing and Generation
LightSeq: A High-Performance Inference Library for Sequence Processing and Generation

LightSeq is a high performance inference library for sequence processing and generation implemented in CUDA. It enables highly efficient computation of modern NLP models such as BERT, GPT2, Transformer, etc. It is therefore best useful for Machine Translation, Text Generation, Dialog, Language Modelling, and other related tasks using these models.

Spert NLP Relation Extraction API deployed with torchserve for inference

SpERT torchserve Spert_torchserve is the Relation Extraction model (SpERT)Span-based Entity and Relation Transformer API deployed with pytorch/serve.

A minimal code for fairseq vq-wav2vec model inference.

vq-wav2vec inference A minimal code for fairseq vq-wav2vec model inference. Runs without installing the fairseq toolkit and its dependencies. Usage ex

Comments
  • Error when I run the sample code

    Error when I run the sample code

    I get this when I try to run the sample code:

    Traceback (most recent call last): File "zerotopics.py", line 1, in from zeroshot_topics import ZeroShotTopicFinder File "/Users/scharlesworth/opt/anaconda3/envs/text_analytics/lib/python3.7/site-packages/zeroshot_topics/init.py", line 3, in from .zeroshot_tm import ZeroShotTopicFinder File "/Users/scharlesworth/opt/anaconda3/envs/text_analytics/lib/python3.7/site-packages/zeroshot_topics/zeroshot_tm.py", line 3, in from .utils import load_zeroshot_model File "/Users/scharlesworth/opt/anaconda3/envs/text_analytics/lib/python3.7/site-packages/zeroshot_topics/utils.py", line 6, in def load_zeroshot_model(model_name="valhalla/distilbart-mnli-12-6"): File "/Users/scharlesworth/opt/anaconda3/envs/text_analytics/lib/python3.7/functools.py", line 490, in lru_cache raise TypeError('Expected maxsize to be an integer or None') TypeError: Expected maxsize to be an integer or None

    Specifics: Python version 3.7.9

    pip freeze gives (yeh this virtualenv is getting big :):

    absl-py==1.0.0 aiohttp==3.8.1 aiosignal==1.2.0 alabaster==0.7.12 aniso8601==9.0.1 antlr4-python3-runtime==4.8 appnope @ file:///opt/concourse/worker/volumes/live/4f734db2-9ca8-4d8b-5b29-6ca15b4b4772/volume/appnope_1606859466979/work async-timeout==4.0.2 asynctest==0.13.0 attrs==20.3.0 Babel==2.9.1 backcall @ file:///home/ktietz/src/ci/backcall_1611930011877/work bertopic==0.6.0 blis @ file:///opt/concourse/worker/volumes/live/cd6a6bea-d063-4b62-4c10-fcc89b17d0ac/volume/cython-blis_1594246851083/work boto3==1.17.86 botocore==1.20.86 brotlipy==0.7.0 cachetools==4.2.1 catalogue==2.0.6 certifi==2020.12.5 cffi @ file:///opt/concourse/worker/volumes/live/2aa8abfe-8b8d-4889-78d9-837b74c3cd64/volume/cffi_1606255119410/work chardet @ file:///opt/concourse/worker/volumes/live/9efbf151-b45b-463d-6340-a5c399bf00b7/volume/chardet_1607706825988/work charset-normalizer==2.0.9 click==7.1.2 colorama==0.4.4 coloredlogs==15.0.1 commonmark==0.9.1 cryptography @ file:///opt/concourse/worker/volumes/live/41c3d62a-f1f8-46ce-414a-9adaf4ea7d96/volume/cryptography_1607636752064/work cycler==0.10.0 cymem @ file:///opt/concourse/worker/volumes/live/3e8d7428-f57d-4000-44e7-34ac8a744f13/volume/cymem_1605062299053/work Cython==0.29.23 dataclasses==0.6 datasets==1.17.0 decorator @ file:///home/ktietz/src/ci/decorator_1611930055503/work dill==0.3.4 docformatter==1.4 docutils==0.15.2 emoji==1.6.1 en-core-web-lg @ https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.2.0/en_core_web_lg-3.2.0-py3-none-any.whl en-core-web-md @ https://github.com/explosion/spacy-models/releases/download/en_core_web_md-3.2.0/en_core_web_md-3.2.0-py3-none-any.whl en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.2.0/en_core_web_sm-3.2.0-py3-none-any.whl en-core-web-trf @ https://github.com/explosion/spacy-models/releases/download/en_core_web_trf-3.2.0/en_core_web_trf-3.2.0-py3-none-any.whl et-xmlfile==1.1.0 fairscale==0.4.4 Faker==8.16.0 fasttext @ file:///Users/scharlesworth/fastText-0.9.2 filelock==3.0.12 flake8==4.0.1 flake8-bugbear==21.11.29 Flask==2.0.2 Flask-Cors==3.0.10 Flask-RESTful==0.3.9 frozenlist==1.2.0 fsspec==2021.11.1 future==0.18.2 gitdb==4.0.9 gitdb2==4.0.2 GitPython==3.1.24 google-api-core==1.26.2 google-api-python-client==2.0.2 google-auth==1.28.0 google-auth-httplib2==0.1.0 google-auth-oauthlib==0.4.6 googleapis-common-protos==1.53.0 grpcio==1.43.0 hdbscan==0.8.27 httplib2==0.19.0 huggingface-hub==0.2.1 humanfriendly==10.0 hydra-core==1.1.1 idna @ file:///tmp/build/80754af9/idna_1593446292537/work imagesize==1.3.0 importlib-metadata @ file:///tmp/build/80754af9/importlib-metadata_1602276842396/work importlib-resources==5.4.0 iniconfig==1.1.1 iopath==0.1.9 ipykernel @ file:///opt/concourse/worker/volumes/live/73e8766c-12c3-4f76-62a6-3dea9a7da5b7/volume/ipykernel_1596206701501/work/dist/ipykernel-5.3.4-py3-none-any.whl ipython @ file:///opt/concourse/worker/volumes/live/ac685347-76d6-4904-4b88-886c6a434f22/volume/ipython_1614616430264/work ipython-genutils @ file:///tmp/build/80754af9/ipython_genutils_1606773439826/work itsdangerous==2.0.1 jedi @ file:///opt/concourse/worker/volumes/live/5006b7b5-a924-4788-6cfe-ae05d8be8830/volume/jedi_1606932947370/work Jinja2==3.0.1 jmespath==0.10.0 joblib==1.0.1 jsonlines==3.0.0 jsonschema==3.0.2 jupyter-client @ file:///tmp/build/80754af9/jupyter_client_1601311786391/work jupyter-core @ file:///opt/concourse/worker/volumes/live/a699b83f-e941-4170-5136-bf87e3f37756/volume/jupyter_core_1612213304212/work keybert==0.5.0 kiwisolver==1.3.1 langcodes==3.3.0 llvmlite==0.36.0 loguru==0.5.3 Markdown==3.3.4 markdown-it-py==0.5.8 MarkupSafe==2.0.1 matplotlib==3.4.0 mccabe==0.6.1 mkl-fft==1.2.0 mkl-random==1.1.1 mkl-service==2.3.0 mock==4.0.3 multidict==5.2.0 multiprocess==0.70.12.2 murmurhash @ file:///opt/concourse/worker/volumes/live/9a0582f9-9097-4dab-6d7a-fcf62b4968ae/volume/murmurhash_1607456116622/work myst-parser==0.12.10 nltk==3.6.5 numba==0.53.1 numpy==1.20.2 oauthlib==3.1.1 omegaconf==2.1.1 openai==0.6.3 openpyxl==3.0.9 packaging==20.9 pandas==1.2.1 parlai==1.5.1 parquet==1.3.1 parso==0.7.0 pathy==0.6.1 pexpect @ file:///tmp/build/80754af9/pexpect_1605563209008/work pickleshare @ file:///tmp/build/80754af9/pickleshare_1606932040724/work Pillow==8.2.0 plac @ file:///opt/concourse/worker/volumes/live/a94b6881-2d18-4055-5a3c-f24036f05ef6/volume/plac_1594259982880/work pluggy==1.0.0 ply==3.11 portalocker==2.3.2 praw==7.1.0 prawcore==1.5.0 preshed @ file:///opt/concourse/worker/volumes/live/952fa955-acc7-4aa0-6766-86f802ea8ef1/volume/preshed_1608233410312/work prompt-toolkit @ file:///tmp/build/80754af9/prompt-toolkit_1616415428029/work protobuf==3.15.6 ptyprocess @ file:///tmp/build/80754af9/ptyprocess_1609355006118/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl py==1.11.0 py-gfm==1.0.2 py-rouge==1.1 py4j==0.10.7 pyarrow==6.0.1 pyasn1==0.4.8 pyasn1-modules==0.2.8 pybind11==2.6.1 pycodestyle==2.8.0 pycparser @ file:///tmp/build/80754af9/pycparser_1594388511720/work pydantic==1.8.2 pyee==8.2.2 pyflakes==2.4.0 Pygments @ file:///tmp/build/80754af9/pygments_1615143339740/work PyJWT==2.3.0 pynndescent==0.5.2 pyodbc==4.0.32 pyOpenSSL @ file:///tmp/build/80754af9/pyopenssl_1608057966937/work pyparsing==2.4.7 pyrsistent @ file:///opt/concourse/worker/volumes/live/656e0c1b-ef87-4251-4a51-1290b2351993/volume/pyrsistent_1600141745371/work PySocks @ file:///opt/concourse/worker/volumes/live/ef943889-94fc-4539-798d-461c60b77804/volume/pysocks_1605305801690/work pytest==6.2.5 pytest-datadir==1.3.1 pytest-regressions==2.2.0 python-dateutil @ file:///home/ktietz/src/ci/python-dateutil_1611928101742/work python-slugify==5.0.2 pytorch-transformers==1.2.0 pytz==2020.5 PyYAML==6.0 pyzmq==20.0.0 regex==2021.11.10 requests @ file:///tmp/build/80754af9/requests_1608241421344/work requests-mock==1.9.3 requests-oauthlib==1.3.0 requests-toolbelt==0.9.1 rich==10.16.2 rsa==4.7.2 s3transfer==0.4.2 sacremoses==0.0.44 scikit-learn==0.24.1 scipy==1.6.2 seaborn==0.11.1 sentence-transformers==1.0.4 sentencepiece==0.1.91 seqeval==0.0.5 sh==1.14.2 six @ file:///opt/concourse/worker/volumes/live/f983ba11-c9fe-4dff-7ce7-d89b95b09771/volume/six_1605205318156/work sklearn==0.0 slack-bolt==1.11.1 slack-sdk==3.13.0 slackclient==2.9.3 slackeventsapi==3.0.1 smart-open==5.2.1 smmap==5.0.0 snowballstemmer==2.2.0 spacy==3.2.0 spacy-alignments==0.8.4 spacy-legacy==3.0.8 spacy-loggers==1.0.1 spacy-sentence-bert==0.1.2 spacy-transformers==1.1.2 spark-nlp==3.0.2 Sphinx==2.2.2 sphinx-autodoc-typehints==1.10.3 sphinx-rtd-theme==1.0.0 sphinxcontrib-applehelp==1.0.2 sphinxcontrib-devhelp==1.0.2 sphinxcontrib-htmlhelp==2.0.0 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==1.0.3 sphinxcontrib-serializinghtml==1.1.5 srsly==2.4.2 subword-nmt==0.3.8 tensorboard==2.7.0 tensorboard-data-server==0.6.1 tensorboard-plugin-wit==1.8.0 tensorboardX==2.4.1 text-unidecode==1.3 thinc==8.0.13 threadpoolctl==2.1.0 thriftpy2==0.4.14 tokenizers==0.10.2 toml==0.10.2 torch==1.10.1 torchtext==0.11.1 tornado @ file:///opt/concourse/worker/volumes/live/d531d395-893c-4ca1-6a5f-717b318eb08c/volume/tornado_1606942307627/work tqdm==4.62.3 traitlets @ file:///home/ktietz/src/ci/traitlets_1611929699868/work transformers==4.11.0 typer==0.4.0 typing-extensions==3.7.4.3 umap-learn==0.5.1 Unidecode==1.3.2 untokenize==0.1.1 update-checker==0.18.0 uritemplate==3.0.1 urllib3==1.26.7 wasabi==0.8.2 wcwidth @ file:///tmp/build/80754af9/wcwidth_1593447189090/work webexteamsbot==0.1.4.2 webexteamssdk==1.6 websocket-client==0.57.0 websocket-server==0.6.4 Werkzeug==2.0.1 xlrd==2.0.1 xxhash==2.0.2 yarl==1.7.2 zeroshot-topics==0.1.0 zipp @ file:///tmp/build/80754af9/zipp_1604001098328/work

    opened by sdcharle 1
  • Add size to lru_cache

    Add size to lru_cache

    /usr/local/lib/python3.7/dist-packages/zeroshot_topics/__init__.py in <module>()
          1 __version__ = '0.1.0'
          2 
    ----> 3 from .zeroshot_tm import ZeroShotTopicFinder
    
    /usr/local/lib/python3.7/dist-packages/zeroshot_topics/zeroshot_tm.py in <module>()
          1 import attr
          2 from keybert import KeyBERT
    ----> 3 from .utils import load_zeroshot_model
          4 from nltk.corpus import wordnet as wn
          5 
    
    /usr/local/lib/python3.7/dist-packages/zeroshot_topics/utils.py in <module>()
          4 
          5 @lru_cache
    ----> 6 def load_zeroshot_model(model_name="valhalla/distilbart-mnli-12-6"):
          7     classifier = pipeline("zero-shot-classification", model=model_name)
          8     return classifier
    
    /usr/lib/python3.7/functools.py in lru_cache(maxsize, typed)
        488             maxsize = 0
        489     elif maxsize is not None:
    --> 490         raise TypeError('Expected maxsize to be an integer or None')
        491 
        492     def decorating_function(user_function):
    
    TypeError: Expected maxsize to be an integer or None
    

    I assume that you have to provide, maxsize parameter to lru_cache. Worked for me, when I provided the parameter.

    opened by gsasikiran 6
Releases(v.0.0.1)
Owner
Rita Anjana
ML engineer
Rita Anjana
✨Fast Coreference Resolution in spaCy with Neural Networks

✨ NeuralCoref 4.0: Coreference Resolution in spaCy with Neural Networks. NeuralCoref is a pipeline extension for spaCy 2.1+ which annotates and resolv

Hugging Face 2.6k Jan 04, 2023
Artificial Conversational Entity for queries in Eulogio "Amang" Rodriguez Institute of Science and Technology (EARIST)

🤖 Coeus - EARIST A.C.E 💬 Coeus is an Artificial Conversational Entity for queries in Eulogio "Amang" Rodriguez Institute of Science and Technology,

Dids Irwyn Reyes 3 Oct 14, 2022
All the code I wrote for Overwatch-related projects that I still own the rights to.

overwatch_shit.zip This is (eventually) going to contain all the software I wrote during my five-year imprisonment stay playing Overwatch. I'll be add

zkxjzmswkwl 2 Dec 31, 2021
An easy-to-use Python module that helps you to extract the BERT embeddings for a large text dataset (Bengali/English) efficiently.

An easy-to-use Python module that helps you to extract the BERT embeddings for a large text dataset (Bengali/English) efficiently.

Khalid Saifullah 37 Sep 05, 2022
Korean extractive summarization. 2021 AI 텍스트 요약 온라인 해커톤 화성갈끄니까팀 코드

korean extractive summarization 2021 AI 텍스트 요약 온라인 해커톤 화성갈끄니까팀 코드 Leaderboard Notice Text Summarization with Pretrained Encoders에 나오는 bertsumext모델(ext

3 Aug 10, 2022
Code for the ACL 2021 paper "Structural Guidance for Transformer Language Models"

Structural Guidance for Transformer Language Models This repository accompanies the paper, Structural Guidance for Transformer Language Models, publis

International Business Machines 10 Dec 14, 2022
Kerberoast with ACL abuse capabilities

targetedKerberoast targetedKerberoast is a Python script that can, like many others (e.g. GetUserSPNs.py), print "kerberoast" hashes for user accounts

Shutdown 213 Dec 22, 2022
Awesome Treasure of Transformers Models Collection

💁 Awesome Treasure of Transformers Models for Natural Language processing contains papers, videos, blogs, official repo along with colab Notebooks. 🛫☑️

Ashish Patel 577 Jan 07, 2023
A very simple framework for state-of-the-art Natural Language Processing (NLP)

A very simple framework for state-of-the-art NLP. Developed by Humboldt University of Berlin and friends. Flair is: A powerful NLP library. Flair allo

flair 12.3k Jan 02, 2023
내부 작업용 django + vue(vuetify) boilerplate. 짠 하면 돌아감.

Pocket Galaxy 아주 간단한 개인용, 혹은 내부용 툴을 만들어야하는데 이왕이면 웹이 편하죠? 그럴때를 위해 만들어둔 django와 vue(vuetify)로 이뤄진 boilerplate 입니다. 각 폴더에 있는 설명서대로 실행을 시키면 일단 당장 뭔가가 돌아갑니

Jamie J. Seol 16 Dec 03, 2021
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par

Computational Linguistics Research Group 8.4k Dec 30, 2022
NLP command-line assistant powered by OpenAI

NLP command-line assistant powered by OpenAI

Axel 16 Dec 09, 2022
Implementation of COCO-LM, Correcting and Contrasting Text Sequences for Language Model Pretraining, in Pytorch

COCO LM Pretraining (wip) Implementation of COCO-LM, Correcting and Contrasting Text Sequences for Language Model Pretraining, in Pytorch. They were a

Phil Wang 44 Jul 28, 2022
CredData is a set of files including credentials in open source projects

CredData is a set of files including credentials in open source projects. CredData includes suspicious lines with manual review results and more information such as credential types for each suspicio

Samsung 19 Sep 07, 2022
Deduplication is the task to combine different representations of the same real world entity.

Deduplication is the task to combine different representations of the same real world entity. This package implements deduplication using active learning. Active learning allows for rapid training wi

63 Nov 17, 2022
🕹 An esoteric language designed so that the program looks like the transcript of a Pokémon battle

PokéBattle is an esoteric language designed so that the program looks like the transcript of a Pokémon battle. Original inspiration and specification

Eduardo Correia 9 Jan 11, 2022
A collection of GNN-based fake news detection models.

This repo includes the Pytorch-Geometric implementation of a series of Graph Neural Network (GNN) based fake news detection models. All GNN models are implemented and evaluated under the User Prefere

SafeGraph 251 Jan 01, 2023
An Explainable Leaderboard for NLP

ExplainaBoard: An Explainable Leaderboard for NLP Introduction | Website | Download | Backend | Paper | Video | Bib Introduction ExplainaBoard is an i

NeuLab 319 Dec 20, 2022
PRAnCER is a web platform that enables the rapid annotation of medical terms within clinical notes.

PRAnCER (Platform enabling Rapid Annotation for Clinical Entity Recognition) is a web platform that enables the rapid annotation of medical terms within clinical notes. A user can highlight spans of

Sontag Lab 39 Nov 14, 2022
GNES enables large-scale index and semantic search for text-to-text, image-to-image, video-to-video and any-to-any content form

GNES is Generic Neural Elastic Search, a cloud-native semantic search system based on deep neural network.

GNES.ai 1.2k Jan 06, 2023