thinc | refreshing functional take on deep learning | Machine Learning library

 by   explosion Python Version: 9.0.0.dev4 License: MIT

kandi X-RAY | thinc Summary

kandi X-RAY | thinc Summary

thinc is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. thinc has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install thinc' or download it from GitHub, PyPI.

🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
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            kandi-support Support

              thinc has a medium active ecosystem.
              It has 2699 star(s) with 271 fork(s). There are 84 watchers for this library.
              There were 3 major release(s) in the last 6 months.
              There are 16 open issues and 116 have been closed. On average issues are closed in 68 days. There are 7 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of thinc is 9.0.0.dev4

            kandi-Quality Quality

              thinc has 0 bugs and 0 code smells.

            kandi-Security Security

              thinc has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              thinc code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              thinc is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              thinc releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              thinc saves you 4494 person hours of effort in developing the same functionality from scratch.
              It has 13582 lines of code, 1606 functions and 171 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed thinc and discovered the below as its top functions. This is intended to give you an instant insight into thinc implemented functionality, and help decide if they suit your requirements.
            • Generates an iterator over the given sequences
            • Convert data to a numpy array
            • Get a batch from a sequence
            • Convert the data into a contig
            • Forward computation
            • Concatenate op
            • Allocates a given shape
            • Creates model with signpost
            • Replace callbacks
            • Chain two layers
            • Forward V2
            • Setup thinc package
            • Flatten an array of Xd
            • Advance Adam algorithm
            • Finish the optimizer
            • Creates a semi - squares model
            • Convert the input tensors to kwargs
            • Load model from bytes
            • Backpropagates x yd
            • Create a new model with the specified range
            • Validate a function on a forward input
            • Resize a linear weighted layer
            • Convert pytorch s default inputs
            • Convert inputs
            • Perform optimizer
            • Convert default inputs
            Get all kandi verified functions for this library.

            thinc Key Features

            No Key Features are available at this moment for thinc.

            thinc Examples and Code Snippets

            copy iconCopy
            python -m spacy download de_trf_bertbasecased_lg
            
            python bert_finetuner_splitset.py de_trf_bertbasecased_lg -o finetuning\output
            
            python -m spacy package finetuning/output /packaged_model
            cd /packaged_model/de_trf_bertbasecased_lg-1.0.0
            python setup.  
            Layer-sequential unit-variance (LSUV) initialization for Keras
            Pythondot img2Lines of Code : 4dot img2License : Permissive (BSD-2-Clause)
            copy iconCopy
            from lsuv_init import LSUVinit
            ...
            batch_size = 32
            model = LSUVinit(model, train_imgs[:batch_size,:,:,:])
              
            Layer-sequential unit-variance (LSUV) initialization for PyTorch
            Pythondot img3Lines of Code : 3dot img3License : Permissive (BSD-2-Clause)
            copy iconCopy
            from LSUV import LSUVinit
            ...
            model = LSUVinit(model,data)
              

            Community Discussions

            QUESTION

            Unknown function registry: 'scorers' with spacy webservice with flask
            Asked 2022-Mar-21 at 12:16

            i'm using spacy in conjunction with flask and anaconda to create a simple webservice. Everything worked fine, until today when i tried to run my code. I got this error and i don't understand what the problem really is. I think this problem has more to do with spacy than flask.

            Here's the code:

            ...

            ANSWER

            Answered 2022-Mar-21 at 12:16

            What you are getting is an internal error from spaCy. You use the en_core_web_trf model provided by spaCy. It's not even a third-party model. It seems to be completely internal to spaCy.

            You could try upgrading spaCy to the latest version.

            The registry name scorers appears to be valid (at least as of spaCy v3.0). See this table: https://spacy.io/api/top-level#section-registry

            The page describing the model you use: https://spacy.io/models/en#en_core_web_trf

            The spacy.load() function documentation: https://spacy.io/api/top-level#spacy.load

            Source https://stackoverflow.com/questions/71556835

            QUESTION

            A weird requirements.txt format
            Asked 2021-Oct-17 at 09:29

            I downloaded a requirements.txt file from a GitHub repository, but it appears to be little different than the normal format of requirements.txt file.

            1. Can you tell me how the author generated this kind of requirements.txt file? Which tools did they use?
            2. How can I use this particular file format to instantiate the Python environment? I have tried executing the commands conda install --file requirements.txt and pip install -r requirements.txt on a Windows ‘ machine, but to no avail.

            https://github.com/wvangansbeke/Unsupervised-Classification/blob/master/requirements.txt

            ...

            ANSWER

            Answered 2021-Oct-17 at 01:46

            This looks like a conda environment.yml file. It can be used to create a conda environment, like so

            Source https://stackoverflow.com/questions/69600759

            QUESTION

            Multipoint(df['geometry']) key error from dataframe but key exist. KeyError: 13 geopandas
            Asked 2021-Oct-11 at 14:51

            data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data

            I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.

            ...

            ANSWER

            Answered 2021-Oct-11 at 14:21

            geopandas 0.10.1

            • have noted that your data is on kaggle, so start by sourcing it
            • there really is only one issue shapely.geometry.MultiPoint() constructor does not work with a filtered series. Pass it a numpy array instead and it works.
            • full code below, have randomly selected a point to serve as gpdPoint

            Source https://stackoverflow.com/questions/69521034

            QUESTION

            Why am I facing PermissionError while installing SpaCy en_core_web_sm on SSH server
            Asked 2021-Aug-26 at 05:02

            I am working on a project and have shifted my environment from local windows to a linux server (via SSH). I only have limited access as the host server is from my college, I've installed many packages without issues (both with and without virtualenv). I'm working on Python 3.6.9.

            I was able to install spacy and import it but I need to use the en_core_web_sm package which has to be installed additionally using the command python3 -m spacy download en_core_web_sm. However, I consistently face a PermissionError as seen in the logs below.

            Why am I facing this error? Is it because I don't have administrator access on the /usr level (refer to last line of logs)? If yes, how come only this package in particular requires a higher level access? If no, are there any workaround for me to install the package, or do I need to contact the server administrator?

            ...

            ANSWER

            Answered 2021-Aug-25 at 08:45

            It seems all other packages are installed under your user /home/jiayi/.local/python3.6/lib and this one tries to install itself globally in /usr/local/lib/python3.6/, not sure why. I guess you can give it installation folder or something. Look here Where does spacy language model download?

            Source https://stackoverflow.com/questions/68919242

            QUESTION

            Adding tagger to blank English spacy pipeline
            Asked 2021-Aug-02 at 15:18

            I am having a hard time figuring out how to assemble spacy pipelines bit by bit from built in models in spacy V3. I have downloaded the en_core_web_sm model and can load it with nlp = spacy.load("en_core_web_sm"). Processing of sample text works just fine like this.

            Now what I want though is to build an English pipeline from blank and add components bit by bit. I do NOT want to load the entire en_core_web_sm pipeline and exclude components. For the sake of concreteness let's say I only want the spacy default tagger in the pipeline. The documentation suggests to me that

            ...

            ANSWER

            Answered 2021-Aug-02 at 14:09

            nlp.add_pipe("tagger") adds a new blank/uninitialized tagger, not the tagger from en_core_web_sm or any other pretrained pipeline. If you add the tagger this way, you need to initialize and train it before you can use it.

            You can add a component from an existing pipeline using the source option:

            Source https://stackoverflow.com/questions/68618759

            QUESTION

            KeyError: 'packaging' auto-fill the config file spacy BERT model
            Asked 2021-May-28 at 10:10

            i'm following this tutorial https://towardsdatascience.com/how-to-fine-tune-bert-transformer-with-spacy-3-6a90bfe57647 and in the part where i have to use this command

            ...

            ANSWER

            Answered 2021-May-28 at 10:10

            Sorry you ran into that, we've had one report of that error before. It seems like something is weird with cupy on colab specifically. Based on the previous report, you should start with a clean Python environment and should not install cupy directly. I think colab uses a special version or something.

            Source https://stackoverflow.com/questions/67720450

            QUESTION

            Custom model for spacy 3.0 implemented using tensorflow
            Asked 2021-May-11 at 13:53

            I have searched and searched. I was able to find this git repository that puts a thinc model as a relationship extractor pipeline in spacy. I need to add my NER model which is implemented using Tensorflow as a Spacy pipeline and I don't know what is the difference between adding a custom model implemented using thinc and with TensorFlow?

            ...

            ANSWER

            Answered 2021-May-05 at 08:52

            Just to clarify: the repository you linked does not showcase a Pytorch model for relation extraction in spaCy - in fact it uses the ML library Thinc to implement the model. You can find more details on that in the corresponding video tutorial.

            The key point to remember is that spaCy works with Thinc models under the hood, but Thinc provides wrappers for Pytorch and Tensorflow.

            To use those in spaCy, you can follow the documentation here. In a nutshell, you should be able to do something like this:

            Source https://stackoverflow.com/questions/67397145

            QUESTION

            Error when loading pipelines in spaCy 3.0
            Asked 2021-May-11 at 11:57

            After updating to spaCy 3.0.6 I haven't been able to load in either of the trained pipelines, although both seem to be properly installed:

            ...

            ANSWER

            Answered 2021-May-11 at 11:57

            It looks like this is fixed in newer versions of transformers (https://github.com/huggingface/transformers/pull/8979). Try upgrading both transformers and spacy-transformers.

            Source https://stackoverflow.com/questions/67484484

            QUESTION

            Packaging SpaCy Model with Pyinstaller: E050 Can't find model
            Asked 2021-May-10 at 15:41

            I'm using Pyinstaller to pack my python spacy code. I'm using the de_core_news_sm and installed it via pip. The normal script performs as expected but as soon as it is packaged with pyinstaller it can not find the model [E050] Can't find model 'de_core_news_sm'. It doesn't seem to be a Python package or a valid path to a data directory. i got for each hook a file:

            ...

            ANSWER

            Answered 2021-May-10 at 15:41

            Adding this to my runtime scripts solve the problem. Instead of loading it as a module i'm loading my model from the path

            Source https://stackoverflow.com/questions/67354667

            QUESTION

            Can't find SpaCy model when packaging with PyInstaller
            Asked 2021-May-01 at 17:26

            I am using PyInstaller package a python script into an .exe. This script is using spacy to load up the following model: en_core_web_sm. I have already run python -m spacy download en_core_web_sm to download the model locally. The issue is when PyInstaller tries to package up my script it can't find the model. I get the following error: Can't find model 'en_core_web_sm'. It doesn't seem to be a Python package or a valid path to a data directory. I thought maybe this meant that I needed to run the download command in my python script in order to make sure it has the model, but if I have my script download the model it just says the requirements are already satisfied. I also have a hook file that handles bringing in hidden imports and is supposed to bring in the model as well:

            ...

            ANSWER

            Answered 2021-Mar-08 at 00:59

            When you use PyInstaller to collect data files into the bundle as you are doing here, the files are actually compiled into the resulting exe itself. This is transparently handled for Python code by PyInstaller when import statements are evaluated.

            However, for data files you must handle this yourself. For instance, spacy is likely looking for the model in the current working directory. It won’t find your model because it is compiled into the .exe instead and therefore isn’t present in the current working directory.

            You will need to use this API:

            https://pyinstaller.readthedocs.io/en/stable/spec-files.html#using-data-files-from-a-module

            This allows you to read a data file from the exe that PyInstaller creates. You can then write it to the current working directory and then spacy should be able to find it.

            Source https://stackoverflow.com/questions/66495437

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install thinc

            Thinc is compatible with Python 3.6+ and runs on Linux, macOS and Windows. The latest releases with binary wheels are available from pip. Before you install Thinc and its dependencies, make sure that your pip, setuptools and wheel are up to date. For the most recent releases, pip 19.3 or newer is recommended. See the extended installation docs for details on optional dependencies for different backends and GPU. You might also want to set up static type checking to take advantage of Thinc's type system. ⚠️ If you have installed PyTorch and you are using Python 3.7+, uninstall the package dataclasses with pip uninstall dataclasses, since it may have been installed by PyTorch and is incompatible with Python 3.7+.

            Support

            Everything you need to know. Thinc's conceptual model and how it works. How to compose models and update state. Thinc's config system and function registry. Interoperability with machine learning frameworks. Weights layers, transforms, combinators and wrappers. Type-check your model definitions and more.
            Find more information at:

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            Install
          • PyPI

            pip install thinc

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            https://github.com/explosion/thinc.git

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            gh repo clone explosion/thinc

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            git@github.com:explosion/thinc.git

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