A blank pipeline is typically just a tokenizer. You might want to create a blankpipeline when you only need a tokenizer, when you want to add more componentsfrom scratch, or for testing purposes. Initializing the language object directlyyields the same result as generating it using spacy.blank(). In both cases thedefault configuration for the chosen language is loaded, and no pretrainedcomponents will be available.
spaCy currently provides support for the following languages. You can help byimproving the existing language dataand extending the tokenization patterns.See here for details on how tocontribute to development. Also see thetraining documentation for how to train your own pipelines onyour data.
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spaCy also supports pipelines trained on more than one language. This isespecially useful for named entity recognition. The language ID used formulti-language or language-neutral pipelines is xx. The language class, ageneric subclass containing only the base language data, can be found inlang/xx.
To train a pipeline using the neutral multi-language class, you can setlang = "xx" in your training config. You can also\import the MultiLanguage class directly, or callspacy.blank("xx") for lazy-loading.
The initialization settings are typically provided in thetraining config and the data is loaded in beforetraining and serialized with the model. This allows you to load the data from alocal path and save out your pipeline and config, without requiring the samelocal path at runtime. See the usage guide on theconfig lifecycle for more background onthis.
The Chinese pipelines provided by spaCy include a custom pkusegmodel trained only onChinese OntoNotes 5.0, since themodels provided by pkuseg include data restricted to research use. Forresearch use, pkuseg provides models for several different domains ("mixed"(equivalent to "default" from pkuseg packages), "news" "web","medicine", "tourism") and for other uses, pkuseg provides a simpletraining API:
The Japanese language class usesSudachiPy for wordsegmentation and part-of-speech tagging. The default Japanese language class andthe provided Japanese pipelines use SudachiPy split mode A. The tokenizerconfig can be used to configure the split mode to A, B or C.
If you run into errors related to sudachipy, which is currently under activedevelopment, we suggest downgrading to sudachipy==0.4.9, which is the versionused for training the current Japanese pipelines.
Note that as of spaCy v3.0, shortcut links like en that create (potentiallybrittle) symlinks in your spaCy installation are deprecated. To downloadand load an installed pipeline package, use its full name:
Pretrained pipeline distributions are hosted onGithub Releases, and youcan find download links there, as well as on the model page. You can also getURLs directly from the command line by using spacy info with the --urlflag, which may be useful for automation.
In some cases, you might prefer downloading the data manually, for example toplace it into a custom directory. You can download the package via your browserfrom the latest releases,or configure your own download script using the URL of the archive file. Thearchive consists of a package directory that contains another directory with thepipeline data.
Since the spacy download command installs the pipeline asa Python package, we always recommend running it from the command line, justlike you install other Python packages with pip install. However, if you needto, or if you want to integrate the download process into another CLI command,you can also import and call the download function used by the CLI via Python.
Keep in mind that the download command installs a Python package into yourenvironment. In order for it to be found after installation, you will need torestart or reload your Python process so that new packages are recognized.
spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. If your application needs to process entire web dumps, spaCy is the library you want to be using.
Since its release in 2015, spaCy has become an industry standard with a huge ecosystem. Choose from a variety of plugins, integrate with your machine learning stack and build custom components and workflows.
Prodigy is an annotation tool so efficient that data scientists can do the annotation themselves, enabling a new level of rapid iteration. Whether you're working on entity recognition, intent detection or image classification, Prodigy can help you train and evaluate your models faster.
spaCy v3.0 introduces a comprehensive and extensible system for configuring your training runs. Your configuration file will describe every detail of your training run, with no hidden defaults, making it easy to rerun your experiments and track changes. You can use the quickstart widget or the init config command to get started, or clone a project template for an end-to-end workflow.
spaCy's new project system gives you a smooth path from prototype to production. It lets you keep track of all those data transformation, preprocessing and training steps, so you can make sure your project is always ready to hand over for automation. It features source asset download, command execution, checksum verification, and caching with a variety of backends and integrations.
spaCy v3.0 introduces transformer-based pipelines that bring spaCy's accuracy right up to the current state-of-the-art. You can also use a CPU-optimized pipeline, which is less accurate but much cheaper to run.
I'm working on a NER custom project and I managed to train a blank spacy model and CamemBERT transformer and compare between them, now I have to write some documentation about both of those models, I did a research and I found out that the blank model is based on CNN and LSTM but there are no details about the layers and the parameters that were used in that architecture. and the same for the transformer.
So can anyone help me with some resources?
yeah, it's just a comparison to show that using a pre-trained model gives better results than the non-pretained one. but it's not the goal of the project it's just an observation, the goal is to create a NER model with good accuracy so I will be able to use it in an application.
Ah okay, in that case, you definitely want to be using spaCy v3 because it'll let you train two models using the same architecture and settings but one with pretrained embeddings (e.g. CammemBERT) and one without, and maybe another one with just word vectors as features. This way, you can have a meaningful comparison, because the only variance between the experiments is the pretrained embeddings that are used.
For some of the categories you describe, you might actually want to try a rule-based approach using spaCy's Matcher (see here for details), especially if the phrases you're looking for follow a consistent pattern. You might also want to explore predicting broader categories and then using other features like the dependency parse to extract the information you need. For example, you could train a category BANK, which would apply to "Bank of America" and then look for the syntactic parent (e.g. "debig card" or "account" etc.). See here for the visualized example:
I explain this approach in more detail in this thread. How you end up writing these rules obviously depends on your data, but I think you'll be able to achieve much better results this way than if you tried to predict fuzzy categories in one go.
If you haven't seen it already, check out @honnibal's talk on how to define NLP problems and solve them through iteration. It shows some examples of using Prodigy, and discusses approaches for framing different kinds of problems and finding out whether something is an NER task or maybe a better fit for text classification, or a combination of statistical and rule-based systems.
You might also find this video helpful. It shows an end-to-end workflow of using Prodigy to train a new entity type from a handful of seed terms, all the way to a loadable spaCy model. It also shows how to use match patterns to quickly bootstrap more examples of relevant entity candidates:
The en_core_web_sm model is usually a good baseline model to start with: it's small, includes all the pre-trained NER categories, as well as the weights for the tagger and parser. Just keep in mind that if you do need some of the other pre-trained categories, you should always include examples of what the model previously got right when you train it. Otherwise, the model may overfit on the new data and "forget" what it previously knew.
If you don't need any of the other pre-trained capabilities, you can also start off with a blank model. In this example, the blank model is exported to /path/to/blank_en_model, which you can then use as the model argument in Prodigy.
The ner.batch-train recipe lets you define an --output argument, which is the directory the trained model will be exported to. This directory will be a loadable spaCy model, so in order to use and test it, you can pass the directory path to spacy.load. For example, let's say you run the following command to train the model:
How you set up the REST API is up to you. In general, it's recommended to only load the model once, e.g. at the top level (and not on every request). I personally like using the library Hug (which also powers Prodigy's REST API btw). Here's an example: 152ee80cbc
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