spaCy plugin for Transformers , Udify, ELmo, etc.

Related tags

Text Data & NLPspacyspacy-extension
Overview

Camphr - spaCy plugin for Transformers, Udify, Elmo, etc.

Documentation Status Gitter PyPI version test and publish

Camphr is a Natural Language Processing library that helps in seamless integration for a wide variety of techniques from state-of-the-art to conventional ones. You can use Transformers , Udify, ELmo, etc. on spaCy.

Check the documentation for more information.

(For Japanese: https://qiita.com/tamurahey/items/53a1902625ccaac1bb2f)

Features

  • A spaCy plugin - Easily integration for a wide variety of methods
  • Transformers with spaCy - Fine-tuning pretrained model with Hydra. Embedding vector
  • Udify - BERT based multitask model in 75 languages
  • Elmo - Deep contextualized word representations
  • Rule base matching with Aho-Corasick, Regex
  • (for Japanese) KNP

License

Camphr is licensed under Apache 2.0.

Comments
  • NER Problem

    NER Problem

    Hello!

    First of all I would like to thank you for the great work on lib Camphr. It's been very useful to me! Can you help me with this doubt? I used lib to train a name recognition model (ner) but when I load the model using nlp = (spacy.load ("~ / outputs // 2020-04-30 // 22-28-36 // models // 9 "), and I pass a text (doc = nlp (" I live in Brazil ")), I can't get any entity recognition (doc.ents >> ()). Could you tell me why this is happening?

    opened by gabrielluz07 9
  • Gender and number subtags generation

    Gender and number subtags generation

    I was comparing the default morpho-syntactic tags generated by camphr-udify and https://github.com/Hyperparticle/udify.

    import spacy
import stanza
from spacy_conll import ConllFormatter

nlp=spacy.load("en_udify")
conllformatter = ConllFormatter(nlp)
nlp.add_pipe(conllformatter, last=True)

doc=nlp("Mother Teresa devoted her entire life to helping others") 
print(doc._.conll_str)


    1	Mother	Mother	PROPN		_	2	compound	_	_
2	Teresa	Teresa	PROPN		_	3	nsubj	_	_
3	devoted	devote	VERB		_	0	root	_	_
4	her	her	PRON		_	6	nmod:poss	_	_
5	entire	entire	ADJ		_	6	amod	_	_
6	life	life	NOUN		_	3	obj	_	_
7	to	to	SCONJ		_	8	mark	_	_
8	helping	help	VERB		_	3	advcl	_	_
9	others	other	NOUN		_	8	obj	_	SpaceAfter=No

    Tags returned by https://github.com/Hyperparticle/udify, for the same input.

    prediction:  1  Mother  Mother  PROPN   _       Number=Sing     2       compound        _       _
2       Teresa  Teresa  PROPN   _       Number=Sing     3       nsubj   _       _
3       devoted devote  VERB    _       Mood=Ind|Tense=Past|VerbForm=Fin        0       root    _       _
4       her     her     PRON    _       Gender=Fem|Number=Sing|Person=3|Poss=Yes|PronType=Prs   6       nmod:poss      _                                               _
5       entire  entire  ADJ     _       Degree=Pos      6       amod    _       _
6       life    life    NOUN    _       Number=Sing     3       obj     _       _
7       to      to      SCONJ   _       _       8       mark    _       _
8       helping help    VERB    _       VerbForm=Ger    3       advcl   _       _
9       others  other   NOUN    _       Number=Plur     8       obj     _       _

    Gender and number subtags are missing in camphr-udify. Could we have those included by default please?

    thanks, Ranjita

    enhancement 
    opened by ranjita-naik 6
  • Camphr+KNP returns an incorrect dependency tag when using a specific adposition.

    Camphr+KNP returns an incorrect dependency tag when using a specific adposition.

    Hello. I report a problem that is happened when analyzing universal dependencies in Japanese text using KNP. When I use a adposition “から”, camphr returns a following wrong result (that shows the conj dependency tag on NOUN→VERB, but an expectation result is the obl dependency tag on VERB→NOUN).

    例1 例2

    (Note that "再結晶", "留去" are the words I added manually, but other VERB words that existed in the original dictionary such as "除去", "撹拌" generates similarly incorrect results.) Same problems sometimes occur when using an adposition "と".

    But using other adpositions, such as “より”, “にて”, camphr returns a correct result.

    例3 例4

    Environment:

    • Docker(python:3.7-buster)
    • spacy = 2.3.2
    • camphr = 0.6.5
    • pyknp = 0.4.5
    • Juman++ ver.1.02
    • KNP ver.4.19
    opened by undermakingbook 6
  • Python 3.8

    Python 3.8

    Camphr is currently pinned at python < 3.8, is there a specific reason for this and if so, what can we do to help?

    Edit: sorry, I just saw #19, still, what can we do to help?

    opened by Evpok 5
  • Support multi labels textcat pipe for transformers

    Support multi labels textcat pipe for transformers

    closes #9

    • Add TrfForMultiLabelSequenceClassification for multiple text classification.
      • pipe name: transformers_multilabel_sequence_classifier
    • Add docs for fine-tuning multi textcat pipe
      • https://github.com/PKSHATechnology-Research/camphr/blob/feature%2Fmulti-textcat/docs/source/notes/finetune_transformers.rst#multilabel-text-classification
    enhancement 
    opened by tamuhey 5
  • unofficial-udify, allennlp, and transformers conflicting dependencies

    unofficial-udify, allennlp, and transformers conflicting dependencies

    I'm trying to install udify on WSL as shown below.

    $ pip install unofficial-udify==0.3.0 [email protected]://github.com/PKSHATechnology-Research/camphr_models/releases/download/0.7.0/en_udify-0.7.tar.gz

    ERROR: Cannot install unofficial-udify and unofficial-udify==0.3.0 because these package versions have conflicting dependencies.

    The conflict is caused by: unofficial-udify 0.3.0 depends on transformers<3.0.0 and >=2.3.0 allennlp 1.3.0 depends on transformers<4.1 and >=4.0 unofficial-udify 0.3.0 depends on transformers<3.0.0 and >=2.3.0 allennlp 1.2.2 depends on transformers<3.6 and >=3.4 unofficial-udify 0.3.0 depends on transformers<3.0.0 and >=2.3.0 allennlp 1.2.1 depends on transformers<3.5 and >=3.1 unofficial-udify 0.3.0 depends on transformers<3.0.0 and >=2.3.0 allennlp 1.2.0 depends on transformers<3.5 and >=3.1 unofficial-udify 0.3.0 depends on transformers<3.0.0 and >=2.3.0 allennlp 1.1.0 depends on transformers<3.1 and >=3.0

    Is this a known issue? Could you suggest a workaroudn please?

    bug 
    opened by ranjita-naik 3
  • Missing tag information

    Missing tag information

    I noticed that the spacy tag field is empty. Is this a known issue? It looks like Udify supports some level of ufeats tagging (https://universaldependencies.org/u/feat/index.html)? I wonder if I'm supposed to b getting any of this in Spacy and I have a bug in my setup, or if it just isn't implemented yet? Would it be souced in token.tag like I'm thinking (if it does exist)?

    I also noticed that displacy doesn't render the POS info. I am wondering if that is related?

    BTW, just have to say that this is awesome.

    opened by tslater 3
  • ImportError: cannot import name 'load_udify' from 'camphr.pipelines' following the example

    ImportError: cannot import name 'load_udify' from 'camphr.pipelines' following the example

    I followed the example here: https://camphr.readthedocs.io/en/latest/notes/udify.html

    I did only see the 0.7.0 model, so I went with that instead. Anyway, the German and English examples work great, but the Japanese one gives me this error:

    >>> from camphr.pipelines import load_udify
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ImportError: cannot import name 'load_udify' from 'camphr.pipelines' (/home/tyler/camphr/env/lib/python3.8/site-packages/camphr/pipelines/__init__.py)
    opened by tslater 3
  • doc.ents empty, doc.is_nered == False

    doc.ents empty, doc.is_nered == False

    I followed the documentation to fine-tune the bert-base-cased (en) ner model and then made a spacy doc with text "Bob Jones and Barack Obama went up the hill in Wisconsin." but the resulting doc has doc.ents = () and doc.is_nered = False.

    Am I missing something?

    Thank you!

    opened by jack-rory-staunton 3
  • Improvement for サ変 of KNP

    Improvement for サ変 of KNP

    Inside _get_child_dep(c), pos for 名詞,サ変名詞 is changed into VERB when it is followed by AUX. So now I think that _get_dep(tag[0]) should be done after _get_child_dep(c).

    opened by KoichiYasuoka 3
  • Bump transformers from 3.0.2 to 4.1.1

    Bump transformers from 3.0.2 to 4.1.1

    Bumps transformers from 3.0.2 to 4.1.1.

    Release notes

    Sourced from transformers's releases.

    Patch release: better error message & invalid trainer attribute

    This patch releases introduces:

    • A better error message when trying to instantiate a SentencePiece-based tokenizer without having SentencePiece installed. #8881
    • Fixes an incorrect attribute in the trainer. #8996

    Transformers v4.0.0: Fast tokenizers, model outputs, file reorganization

    Transformers v4.0.0-rc-1: Fast tokenizers, model outputs, file reorganization

    Breaking changes since v3.x

    Version v4.0.0 introduces several breaking changes that were necessary.

    1. AutoTokenizers and pipelines now use fast (rust) tokenizers by default.

    The python and rust tokenizers have roughly the same API, but the rust tokenizers have a more complete feature set. The main breaking change is the handling of overflowing tokens between the python and rust tokenizers.

    How to obtain the same behavior as v3.x in v4.x

    In version v3.x:

    from transformers import AutoTokenizer

    tokenizer = AutoTokenizer.from_pretrained("xxx")

    to obtain the same in version v4.x:

    from transformers import AutoTokenizer

    tokenizer = AutoTokenizer.from_pretrained("xxx", use_fast=False)

    2. SentencePiece is removed from the required dependencies

    The requirement on the SentencePiece dependency has been lifted from the setup.py. This is done so that we may have a channel on anaconda cloud without relying on conda-forge. This means that the tokenizers that depend on the SentencePiece library will not be available with a standard transformers installation.

    This includes the slow versions of:

    • XLNetTokenizer
    • AlbertTokenizer
    • CamembertTokenizer
    • MBartTokenizer
    • PegasusTokenizer
    • T5Tokenizer
    • ReformerTokenizer
    • XLMRobertaTokenizer

    How to obtain the same behavior as v3.x in v4.x

    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language
    • @dependabot badge me will comment on this PR with code to add a "Dependabot enabled" badge to your readme

    Additionally, you can set the following in your Dependabot dashboard:

    • Update frequency (including time of day and day of week)
    • Pull request limits (per update run and/or open at any time)
    • Out-of-range updates (receive only lockfile updates, if desired)
    • Security updates (receive only security updates, if desired)
    dependencies 
    opened by dependabot-preview[bot] 2
  • Bump certifi from 2021.5.30 to 2022.12.7 in /packages/camphr_pattern_search

    Bump certifi from 2021.5.30 to 2022.12.7 in /packages/camphr_pattern_search

    Bumps certifi from 2021.5.30 to 2022.12.7.

    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
  • Bump numpy from 1.21.0 to 1.22.0 in /packages/camphr_pattern_search

    Bump numpy from 1.21.0 to 1.22.0 in /packages/camphr_pattern_search

    Bumps numpy from 1.21.0 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
Releases(0.7.0)
Owner
GitHub Repository https://camphr.readthedocs.io/en/latest/
RIDE automatically creates the package and boilerplate OOP Python node scripts as per your needs

RIDE: ROS IDE RIDE automatically creates the package and boilerplate OOP Python code for nodes as per your needs (RIDE is not an IDE, but even ROS isn

Jash Mota 20 Jul 14, 2022
Search msDS-AllowedToActOnBehalfOfOtherIdentity

前言 现在进行RBCD的攻击手段主要是搜索mS-DS-CreatorSID，如果机器的创建者是我们可控的话，那就可以修改对应机器的msDS-AllowedToActOnBehalfOfOtherIdentity，利用工具SharpAllowedToAct-Modify 那我们索性也试试搜索所有计算机

Jumbo 26 Dec 05, 2022
An implementation of WaveNet with fast generation

pytorch-wavenet This is an implementation of the WaveNet architecture, as described in the original paper. Features Automatic creation of a dataset (t

Vincent Herrmann 858 Dec 27, 2022
LOT: A Benchmark for Evaluating Chinese Long Text Understanding and Generation

LOT: A Benchmark for Evaluating Chinese Long Text Understanding and Generation Tasks | Datasets | LongLM | Baselines | Paper Introduction LOT is a ben

46 Dec 28, 2022
PUA Programming Language written in Python.

pua-lang PUA Programming Language written in Python. Installation git clone https://github.com/zhaoyang97/pua-lang.git cd pua-lang pip install . Try

zy 4 Feb 19, 2022
Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning and Hydra.

OpenSpeech provides reference implementations of various ASR modeling papers and three languages recipe to perform tasks on automatic speech recogniti

Soohwan Kim 26 Dec 14, 2022
String Gen + Word Checker

Creates random strings and checks if any of them are a real words. Mostly a waste of time ngl but it is cool to see it work and the fact that it can generate a real random word within10sec

1 Jan 06, 2022
GrammarTagger — A Neural Multilingual Grammar Profiler for Language Learning

GrammarTagger — A Neural Multilingual Grammar Profiler for Language Learning GrammarTagger is an open-source toolkit for grammatical profiling for lan

Octanove Labs 27 Jan 05, 2023
APEACH: Attacking Pejorative Expressions with Analysis on Crowd-generated Hate Speech Evaluation Datasets

APEACH - Korean Hate Speech Evaluation Datasets APEACH is the first crowd-generated Korean evaluation dataset for hate speech detection. Sentences of

Kevin-Yang 70 Dec 06, 2022
This converter will create the exact measure for your cappuccino recipe from the grandiose Rafaella Ballerini!

About CappuccinoJs This converter will create the exact measure for your cappuccino recipe from the grandiose Rafaella Ballerini! Este conversor criar

Arthur Ottoni Ribeiro 48 Nov 15, 2022
A curated list of efficient attention modules

awesome-fast-attention A curated list of efficient attention modules

Sepehr Sameni 891 Dec 22, 2022
Turn clang-tidy warnings and fixes to comments in your pull request

clang-tidy pull request comments A GitHub Action to post clang-tidy warnings and suggestions as review comments on your pull request. What platisd/cla

Dimitris Platis 30 Dec 13, 2022
Official PyTorch Implementation of paper "NeLF: Neural Light-transport Field for Single Portrait View Synthesis and Relighting", EGSR 2021.

NeLF: Neural Light-transport Field for Single Portrait View Synthesis and Relighting Official PyTorch Implementation of paper "NeLF: Neural Light-tran

Ken Lin 38 Dec 26, 2022
AudioCLIP Extending CLIP to Image, Text and Audio

AudioCLIP Extending CLIP to Image, Text and Audio This repository contains implementation of the models described in the paper arXiv:2106.13043. This

458 Jan 02, 2023
Unofficial PyTorch implementation of Google AI's VoiceFilter system

VoiceFilter Note from Seung-won (2020.10.25) Hi everyone! It's Seung-won from MINDs Lab, Inc. It's been a long time since I've released this open-sour

MINDs Lab 881 Jan 03, 2023
A music comments dataset, containing 39,051 comments for 27,384 songs.

Music Comments Dataset A music comments dataset, containing 39,051 comments for 27,384 songs. For academic research use only. Introduction This datase

Zhang Yixiao 2 Jan 10, 2022
Use Tensorflow2.7.0 Build OpenAI'GPT-2

TF2_GPT-2 Use Tensorflow2.7.0 Build OpenAI'GPT-2 使用最新tensorflow2.7.0构建openai官方的GPT-2 NLP模型 优点 使用无监督技术 拥有大量词汇量 可实现续写（堪比“xx梦续写”） 实现对话后续将应用于FloatTech的Bot

Watermelon 9 Sep 13, 2022
Tool to add main subject to items on Wikidata using a WMFs CirrusSearch for named entity recognition or a manually supplied list of QIDs

ItemSubjector Tool made to add main subject statements to items based on the title using a home-brewed CirrusSearch-based Named Entity Recognition alg

Dennis Priskorn 9 Nov 17, 2022
novel deep learning research works with PaddlePaddle

Research 发布基于飞桨的前沿研究工作，包括CV、NLP、KG、STDM等领域的顶会论文和比赛冠军模型。 目录 计算机视觉(Computer Vision) 自然语言处理(Natrual Language Processing) 知识图谱(Knowledge Graph) 时空数据挖掘(Spa

1.5k Jan 03, 2023
A multi-lingual approach to AllenNLP CoReference Resolution along with a wrapper for spaCy.

Crosslingual Coreference Coreference is amazing but the data required for training a model is very scarce. In our case, the available training for non

Pandora Intelligence 71 Jan 04, 2023