explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2025-01-01 12:56:29 +03:00
Code Issues Packages Projects Releases Wiki Activity
a183db3cef
spaCy/website/docs/api/cli.mdx
Daniël de Kok a183db3cef
Merge the parser refactor into v4 (#10940) 
* Try to fix doc.copy

* Set dev version

* Make vocab always own lexemes

* Change version

* Add SpanGroups.copy method

* Fix set_annotations during Parser.update

* Fix dict proxy copy

* Upd version

* Fix copying SpanGroups

* Fix set_annotations in parser.update

* Fix parser set_annotations during update

* Revert "Fix parser set_annotations during update"

This reverts commit eb138c89ed.

* Revert "Fix set_annotations in parser.update"

This reverts commit c6df0eafd0.

* Fix set_annotations during parser update

* Inc version

* Handle final states in get_oracle_sequence

* Inc version

* Try to fix parser training

* Inc version

* Fix

* Inc version

* Fix parser oracle

* Inc version

* Inc version

* Fix transition has_gold

* Inc version

* Try to use real histories, not oracle

* Inc version

* Upd parser

* Inc version

* WIP on rewrite parser

* WIP refactor parser

* New progress on parser model refactor

* Prepare to remove parser_model.pyx

* Convert parser from cdef class

* Delete spacy.ml.parser_model

* Delete _precomputable_affine module

* Wire up tb_framework to new parser model

* Wire up parser model

* Uncython ner.pyx and dep_parser.pyx

* Uncython

* Work on parser model

* Support unseen_classes in parser model

* Support unseen classes in parser

* Cleaner handling of unseen classes

* Work through tests

* Keep working through errors

* Keep working through errors

* Work on parser. 15 tests failing

* Xfail beam stuff. 9 failures

* More xfail. 7 failures

* Xfail. 6 failures

* cleanup

* formatting

* fixes

* pass nO through

* Fix empty doc in update

* Hackishly fix resizing. 3 failures

* Fix redundant test. 2 failures

* Add reference version

* black formatting

* Get tests passing with reference implementation

* Fix missing prints

* Add missing file

* Improve indexing on reference implementation

* Get non-reference forward func working

* Start rigging beam back up

* removing redundant tests, cf #8106

* black formatting

* temporarily xfailing issue 4314

* make flake8 happy again

* mypy fixes

* ensure labels are added upon predict

* cleanup remnants from merge conflicts

* Improve unseen label masking

Two changes to speed up masking by ~10%:

- Use a bool array rather than an array of float32.

- Let the mask indicate whether a label was seen, rather than
  unseen. The mask is most frequently used to index scores for
  seen labels. However, since the mask marked unseen labels,
  this required computing an intermittent flipped mask.

* Write moves costs directly into numpy array (#10163)

This avoids elementwise indexing and the allocation of an additional
array.

Gives a ~15% speed improvement when using batch_by_sequence with size
32.

* Temporarily disable ner and rehearse tests

Until rehearse is implemented again in the refactored parser.

* Fix loss serialization issue (#10600)

* Fix loss serialization issue

Serialization of a model fails with:

TypeError: array(738.3855, dtype=float32) is not JSON serializable

Fix this using float conversion.

* Disable CI steps that require spacy.TransitionBasedParser.v2

After finishing the refactor, TransitionBasedParser.v2 should be
provided for backwards compat.

* Add back support for beam parsing to the refactored parser (#10633)

* Add back support for beam parsing

Beam parsing was already implemented as part of the `BeamBatch` class.
This change makes its counterpart `GreedyBatch`. Both classes are hooked
up in `TransitionModel`, selecting `GreedyBatch` when the beam size is
one, or `BeamBatch` otherwise.

* Use kwarg for beam width

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Avoid implicit default for beam_width and beam_density

* Parser.{beam,greedy}_parse: ensure labels are added

* Remove 'deprecated' comments

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Parser `StateC` optimizations (#10746)

* `StateC`: Optimizations

Avoid GIL acquisition in `__init__`
Increase default buffer capacities on init
Reduce C++ exception overhead

* Fix typo

* Replace `set::count` with `set::find`

* Add exception attribute to c'tor

* Remove unused import

* Use a power-of-two value for initial capacity
Use default-insert to init `_heads` and `_unshiftable`

* Merge `cdef` variable declarations and assignments

* Vectorize `example.get_aligned_parses` (#10789)

* `example`: Vectorize `get_aligned_parse`
Rename `numpy` import

* Convert aligned array to lists before returning

* Revert import renaming

* Elide slice arguments when selecting the entire range

* Tagger/morphologizer alignment performance optimizations (#10798)

* `example`: Unwrap `numpy` scalar arrays before passing them to `StringStore.__getitem__`

* `AlignmentArray`: Use native list as staging buffer for offset calculation

* `example`: Vectorize `get_aligned`

* Hoist inner functions out of `get_aligned`

* Replace inline `if..else` clause in assignment statement

* `AlignmentArray`: Use raw indexing into offset and data `numpy` arrays

* `example`: Replace array unique value check with `groupby`

* `example`: Correctly exclude tokens with no alignment in `_get_aligned_vectorized`
Simplify `_get_aligned_non_vectorized`

* `util`: Update `all_equal` docstring

* Explicitly use `int32_t*`

* Restore C CPU inference in the refactored parser (#10747)

* Bring back the C parsing model

The C parsing model is used for CPU inference and is still faster for
CPU inference than the forward pass of the Thinc model.

* Use C sgemm provided by the Ops implementation

* Make tb_framework module Cython, merge in C forward implementation

* TransitionModel: raise in backprop returned from forward_cpu

* Re-enable greedy parse test

* Return transition scores when forward_cpu is used

* Apply suggestions from code review

Import `Model` from `thinc.api`

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Use relative imports in tb_framework

* Don't assume a default for beam_width

* We don't have a direct dependency on BLIS anymore

* Rename forwards to _forward_{fallback,greedy_cpu}

* Require thinc >=8.1.0,<8.2.0

* tb_framework: clean up imports

* Fix return type of _get_seen_mask

* Move up _forward_greedy_cpu

* Style fixes.

* Lower thinc lowerbound to 8.1.0.dev0

* Formatting fix

Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>

* Reimplement parser rehearsal function (#10878)

* Reimplement parser rehearsal function

Before the parser refactor, rehearsal was driven by a loop in the
`rehearse` method itself. For each parsing step, the loops would:

1. Get the predictions of the teacher.
2. Get the predictions and backprop function of the student.
3. Compute the loss and backprop into the student.
4. Move the teacher and student forward with the predictions of
   the student.

In the refactored parser, we cannot perform search stepwise rehearsal
anymore, since the model now predicts all parsing steps at once.
Therefore, rehearsal is performed in the following steps:

1. Get the predictions of all parsing steps from the student, along
   with its backprop function.
2. Get the predictions from the teacher, but use the predictions of
   the student to advance the parser while doing so.
3. Compute the loss and backprop into the student.

To support the second step a new method, `advance_with_actions` is
added to `GreedyBatch`, which performs the provided parsing steps.

* tb_framework: wrap upper_W and upper_b in Linear

Thinc's Optimizer cannot handle resizing of existing parameters. Until
it does, we work around this by wrapping the weights/biases of the upper
layer of the parser model in Linear. When the upper layer is resized, we
copy over the existing parameters into a new Linear instance. This does
not trigger an error in Optimizer, because it sees the resized layer as
a new set of parameters.

* Add test for TransitionSystem.apply_actions

* Better FIXME marker

Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>

* Fixes from Madeesh

* Apply suggestions from Sofie

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Remove useless assignment

Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Rename some identifiers in the parser refactor (#10935)

* Rename _parseC to _parse_batch

* tb_framework: prefix many auxiliary functions with underscore

To clearly state the intent that they are private.

* Rename `lower` to `hidden`, `upper` to `output`

* Parser slow test fixup

We don't have TransitionBasedParser.{v1,v2} until we bring it back as a
legacy option.

* Remove last vestiges of PrecomputableAffine

This does not exist anymore as a separate layer.

* ner: re-enable sentence boundary checks

* Re-enable test that works now.

* test_ner: make loss test more strict again

* Remove commented line

* Re-enable some more beam parser tests

* Remove unused _forward_reference function

* Update for CBlas changes in Thinc 8.1.0.dev2

Bump thinc dependency to 8.1.0.dev3.

* Remove references to spacy.TransitionBasedParser.{v1,v2}

Since they will not be offered starting with spaCy v4.

* `tb_framework`: Replace references to `thinc.backends.linalg` with `CBlas`

* dont use get_array_module (#11056) (#11293)

Co-authored-by: kadarakos <kadar.akos@gmail.com>

* Move `thinc.extra.search` to `spacy.pipeline._parser_internals` (#11317)

* `search`: Move from `thinc.extra.search`
Fix NPE in `Beam.__dealloc__`

* `pytest`: Add support for executing Cython tests
Move `search` tests from thinc and patch them to run with `pytest`

* `mypy` fix

* Update comment

* `conftest`: Expose `register_cython_tests`

* Remove unused import

* Move `argmax` impls to new `_parser_utils` Cython module (#11410)

* Parser does not have to be a cdef class anymore

This also fixes validation of the initialization schema.

* Add back spacy.TransitionBasedParser.v2

* Fix a rename that was missed in #10878.

So that rehearsal tests pass.

* Remove module from setup.py that got added during the merge

* Bring back support for `update_with_oracle_cut_size` (#12086)

* Bring back support for `update_with_oracle_cut_size`

This option was available in the pre-refactor parser, but was never
implemented in the refactored parser. This option cuts transition
sequences that are longer than `update_with_oracle_cut` size into
separate sequences that have at most `update_with_oracle_cut`
transitions. The oracle (gold standard) transition sequence is used to
determine the cuts and the initial states for the additional sequences.

Applying this cut makes the batches more homogeneous in the transition
sequence lengths, making forward passes (and as a consequence training)
much faster.

Training time 1000 steps on de_core_news_lg:

- Before this change: 149s
- After this change: 68s
- Pre-refactor parser: 81s

* Fix a rename that was missed in #10878.

So that rehearsal tests pass.

* Apply suggestions from @shadeMe

* Use chained conditional

* Test with update_with_oracle_cut_size={0, 1, 5, 100}

And fix a git that occurs with a cut size of 1.

* Fix up some merge fall out

* Update parser distillation for the refactor

In the old parser, we'd iterate over the transitions in the distill
function and compute the loss/gradients on the go. In the refactored
parser, we first let the student model parse the inputs. Then we'll let
the teacher compute the transition probabilities of the states in the
student's transition sequence. We can then compute the gradients of the
student given the teacher.

* Add back spacy.TransitionBasedParser.v1 references

- Accordion in the architecture docs.
- Test in test_parse, but disabled until we have a spacy-legacy release.

Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
Co-authored-by: svlandeg <svlandeg@github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: kadarakos <kadar.akos@gmail.com>
2023-01-18 11:27:45 +01:00

1624 lines
109 KiB
Plaintext
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

---
title: Command Line Interface
teaser: Download, train and package pipelines, and debug spaCy
source: spacy/cli
menu:
- ['download', 'download']
- ['info', 'info']
- ['validate', 'validate']
- ['init', 'init']
- ['convert', 'convert']
- ['debug', 'debug']
- ['train', 'train']
- ['pretrain', 'pretrain']
- ['evaluate', 'evaluate']
- ['apply', 'apply']
- ['find-threshold', 'find-threshold']
- ['assemble', 'assemble']
- ['package', 'package']
- ['project', 'project']
- ['huggingface-hub', 'huggingface-hub']
---
spaCy's CLI provides a range of helpful commands for downloading and training
pipelines, converting data and debugging your config, data and installation. For
a list of available commands, you can type `python -m spacy --help`. You can
also add the `--help` flag to any command or subcommand to see the description,
available arguments and usage.
## download {id="download",tag="command"}
Download [trained pipelines](/usage/models) for spaCy. The downloader finds the
best-matching compatible version and uses `pip install` to download the Python
package. Direct downloads don't perform any compatibility checks and require the
pipeline name to be specified with its version (e.g. `en_core_web_sm-3.0.0`).
> #### Downloading best practices
>
> The `download` command is mostly intended as a convenient, interactive wrapper
> – it performs compatibility checks and prints detailed messages in case things
> go wrong. It's **not recommended** to use this command as part of an automated
> process. If you know which package your project needs, you should consider a
> [direct download via pip](/usage/models#download-pip), or uploading the
> package to a local PyPi installation and fetching it straight from there. This
> will also allow you to add it as a versioned package dependency to your
> project.
```bash
$ python -m spacy download [model] [--direct] [--sdist] [pip_args]
```
| Name | Description |
| ------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model` | Pipeline package name, e.g. [`en_core_web_sm`](/models/en#en_core_web_sm). ~~str (positional)~~ |
| `--direct`, `-D` | Force direct download of exact package version. ~~bool (flag)~~ |
| `--sdist`, `-S` <Tag variant="new">3</Tag> | Download the source package (`.tar.gz` archive) instead of the default pre-built binary wheel. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| pip args | Additional installation options to be passed to `pip install` when installing the pipeline package. For example, `--user` to install to the user home directory or `--no-deps` to not install package dependencies. ~~Any (option/flag)~~ |
| **CREATES** | The installed pipeline package in your `site-packages` directory. |
## info {id="info",tag="command"}
Print information about your spaCy installation, trained pipelines and local
setup, and generate [Markdown](https://en.wikipedia.org/wiki/Markdown)-formatted
markup to copy-paste into
[GitHub issues](https://github.com/explosion/spaCy/issues).
```bash
$ python -m spacy info [--markdown] [--silent] [--exclude]
```
> #### Example
>
> ```bash
> $ python -m spacy info en_core_web_lg --markdown
> ```
```bash
$ python -m spacy info [model] [--markdown] [--silent] [--exclude]
```
| Name | Description |
| -------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
| `model` | A trained pipeline, i.e. package name or path (optional). ~~Optional[str] \(option)~~ |
| `--markdown`, `-md` | Print information as Markdown. ~~bool (flag)~~ |
| `--silent`, `-s` | Don't print anything, just return the values. ~~bool (flag)~~ |
| `--exclude`, `-e` | Comma-separated keys to exclude from the print-out. Defaults to `"labels"`. ~~Optional[str]~~ |
| `--url`, `-u` <Tag variant="new">3.5.0</Tag> | Print the URL to download the most recent compatible version of the pipeline. Requires a pipeline name. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **PRINTS** | Information about your spaCy installation. |
## validate {id="validate",version="2",tag="command"}
Find all trained pipeline packages installed in the current environment and
check whether they are compatible with the currently installed version of spaCy.
Should be run after upgrading spaCy via `pip install -U spacy` to ensure that
all installed packages can be used with the new version. It will show a list of
packages and their installed versions. If any package is out of date, the latest
compatible versions and command for updating are shown.
> #### Automated validation
>
> You can also use the `validate` command as part of your build process or test
> suite, to ensure all packages are up to date before proceeding. If
> incompatible packages are found, it will return `1`.
```bash
$ python -m spacy validate
```
| Name | Description |
| ---------- | -------------------------------------------------------------------- |
| **PRINTS** | Details about the compatibility of your installed pipeline packages. |
## init {id="init",version="3"}
The `spacy init` CLI includes helpful commands for initializing training config
files and pipeline directories.
### init config {id="init-config",version="3",tag="command"}
Initialize and save a [`config.cfg` file](/usage/training#config) using the
**recommended settings** for your use case. It works just like the
[quickstart widget](/usage/training#quickstart), only that it also auto-fills
all default values and exports a [training](/usage/training#config)-ready
config. The settings you specify will impact the suggested model architectures
and pipeline setup, as well as the hyperparameters. You can also adjust and
customize those settings in your config file later.
> #### Example
>
> ```bash
> $ python -m spacy init config config.cfg --lang en --pipeline ner,textcat --optimize accuracy
> ```
```bash
$ python -m spacy init config [output_file] [--lang] [--pipeline] [--optimize] [--gpu] [--pretraining] [--force]
```
| Name | Description |
| ---------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `output_file` | Path to output `.cfg` file or `-` to write the config to stdout (so you can pipe it forward to a file or to the `train` command). Note that if you're writing to stdout, no additional logging info is printed. ~~Path (positional)~~ |
| `--lang`, `-l` | Optional code of the [language](/usage/models#languages) to use. Defaults to `"en"`. ~~str (option)~~ |
| `--pipeline`, `-p` | Comma-separated list of trainable [pipeline components](/usage/processing-pipelines#built-in) to include. Defaults to `"tagger,parser,ner"`. ~~str (option)~~ |
| `--optimize`, `-o` | `"efficiency"` or `"accuracy"`. Whether to optimize for efficiency (faster inference, smaller model, lower memory consumption) or higher accuracy (potentially larger and slower model). This will impact the choice of architecture, pretrained weights and related hyperparameters. Defaults to `"efficiency"`. ~~str (option)~~ |
| `--gpu`, `-G` | Whether the model can run on GPU. This will impact the choice of architecture, pretrained weights and related hyperparameters. ~~bool (flag)~~ |
| `--pretraining`, `-pt` | Include config for pretraining (with [`spacy pretrain`](/api/cli#pretrain)). Defaults to `False`. ~~bool (flag)~~ |
| `--force`, `-f` | Force overwriting the output file if it already exists. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | The config file for training. |
### init fill-config {id="init-fill-config",version="3"}
Auto-fill a partial [.cfg file](/usage/training#config) with **all default
values**, e.g. a config generated with the
[quickstart widget](/usage/training#quickstart). Config files used for training
should always be complete and not contain any hidden defaults or missing values,
so this command helps you create your final training config. In order to find
the available settings and defaults, all functions referenced in the config will
be created, and their signatures are used to find the defaults. If your config
contains a problem that can't be resolved automatically, spaCy will show you a
validation error with more details.
> #### Example
>
> ```bash
> $ python -m spacy init fill-config base.cfg config.cfg --diff
> ```
>
> #### Example diff
>
> ![Screenshot of visual diff in terminal](/images/cli_init_fill-config_diff.jpg)
```bash
$ python -m spacy init fill-config [base_path] [output_file] [--diff]
```
| Name | Description |
| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `base_path` | Path to base config to fill, e.g. generated by the [quickstart widget](/usage/training#quickstart). ~~Path (positional)~~ |
| `output_file` | Path to output `.cfg` file or "-" to write to stdout so you can pipe it to a file. Defaults to "-" (stdout). ~~Path (positional)~~ |
| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--pretraining`, `-pt` | Include config for pretraining (with [`spacy pretrain`](/api/cli#pretrain)). Defaults to `False`. ~~bool (flag)~~ |
| `--diff`, `-D` | Print a visual diff highlighting the changes. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | Complete and auto-filled config file for training. |
### init vectors {id="init-vectors",version="3",tag="command"}
Convert [word vectors](/usage/linguistic-features#vectors-similarity) for use
with spaCy. Will export an `nlp` object that you can use in the
[`[initialize]`](/api/data-formats#config-initialize) block of your config to
initialize a model with vectors. See the usage guide on
[static vectors](/usage/embeddings-transformers#static-vectors) for details on
how to use vectors in your model.
<Infobox title="New in v3.0" variant="warning" id="init-model">
This functionality was previously available as part of the command `init-model`.
</Infobox>
```bash
$ python -m spacy init vectors [lang] [vectors_loc] [output_dir] [--prune] [--truncate] [--name] [--verbose]
```
| Name | Description |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `lang` | Pipeline language [IETF language tag](https://www.w3.org/International/articles/language-tags/), such as `en`. ~~str (positional)~~ |
| `vectors_loc` | Location of vectors. Should be a file where the first row contains the dimensions of the vectors, followed by a space-separated Word2Vec table. File can be provided in `.txt` format or as a zipped text file in `.zip` or `.tar.gz` format. ~~Path (positional)~~ |
| `output_dir` | Pipeline output directory. Will be created if it doesn't exist. ~~Path (positional)~~ |
| `--truncate`, `-t` | Number of vectors to truncate to when reading in vectors file. Defaults to `0` for no truncation. ~~int (option)~~ |
| `--prune`, `-p` | Number of vectors to prune the vocabulary to. Defaults to `-1` for no pruning. ~~int (option)~~ |
| `--mode`, `-m` | Vectors mode: `default` or [`floret`](https://github.com/explosion/floret). Defaults to `default`. ~~Optional[str] \(option)~~ |
| `--name`, `-n` | Name to assign to the word vectors in the `meta.json`, e.g. `en_core_web_md.vectors`. ~~Optional[str] \(option)~~ |
| `--verbose`, `-V` | Print additional information and explanations. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | A spaCy pipeline directory containing the vocab and vectors. |
### init labels {id="init-labels",version="3",tag="command"}
Generate JSON files for the labels in the data. This helps speed up the training
process, since spaCy won't have to preprocess the data to extract the labels.
After generating the labels, you can provide them to components that accept a
`labels` argument on initialization via the
[`[initialize]`](/api/data-formats#config-initialize) block of your config.
> #### Example config
>
> ```ini
> [initialize.components.ner]
>
> [initialize.components.ner.labels]
> @readers = "spacy.read_labels.v1"
> path = "corpus/labels/ner.json
> ```
```bash
$ python -m spacy init labels [config_path] [output_path] [--code] [--verbose] [--gpu-id] [overrides]
```
| Name | Description |
| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. If `-`, the data will be [read from stdin](/usage/training#config-stdin). ~~Union[Path, str] \(positional)~~ |
| `output_path` | Output directory for the label files. Will create one JSON file per component. ~~Path (positional)~~ |
| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--verbose`, `-V` | Show more detailed messages for debugging purposes. ~~bool (flag)~~ |
| `--gpu-id`, `-g` | GPU ID or `-1` for CPU. Defaults to `-1`. ~~int (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ |
| **CREATES** | The label files. |
## convert {id="convert",tag="command"}
Convert files into spaCy's
[binary training data format](/api/data-formats#binary-training), a serialized
[`DocBin`](/api/docbin), for use with the `train` command and other experiment
management functions. The converter can be specified on the command line, or
chosen based on the file extension of the input file.
```bash
$ python -m spacy convert [input_file] [output_dir] [--converter] [--file-type] [--n-sents] [--seg-sents] [--base] [--morphology] [--merge-subtokens] [--ner-map] [--lang]
```
| Name | Description |
| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
| `input_path` | Input file or directory. ~~Path (positional)~~ |
| `output_dir` | Output directory for converted file. Defaults to `"-"`, meaning data will be written to `stdout`. ~~Optional[Path] \(option)~~ |
| `--converter`, `-c` | Name of converter to use (see below). ~~str (option)~~ |
| `--file-type`, `-t` | Type of file to create. Either `spacy` (default) for binary [`DocBin`](/api/docbin) data or `json` for v2.x JSON format. ~~str (option)~~ |
| `--n-sents`, `-n` | Number of sentences per document. Supported for: `conll`, `conllu`, `iob`, `ner` ~~int (option)~~ |
| `--seg-sents`, `-s` | Segment sentences. Supported for: `conll`, `ner` ~~bool (flag)~~ |
| `--base`, `-b`, `--model` | Trained spaCy pipeline for sentence segmentation to use as base (for `--seg-sents`). ~~Optional[str](option)~~ |
| `--morphology`, `-m` | Enable appending morphology to tags. Supported for: `conllu` ~~bool (flag)~~ |
| `--merge-subtokens`, `-T` | Merge CoNLL-U subtokens ~~bool (flag)~~ |
| `--ner-map`, `-nm` | NER tag mapping (as JSON-encoded dict of entity types). Supported for: `conllu` ~~Optional[Path](option)~~ |
| `--lang`, `-l` | Language code (if tokenizer required). ~~Optional[str] \(option)~~ |
| `--concatenate`, `-C` | Concatenate output to a single file ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | Binary [`DocBin`](/api/docbin) training data that can be used with [`spacy train`](/api/cli#train). |
### Converters {id="converters"}
| ID | Description |
| --------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `auto` | Automatically pick converter based on file extension and file content (default). |
| `json` | JSON-formatted training data used in spaCy v2.x. |
| `conllu` | Universal Dependencies `.conllu` format. |
| `ner` / `conll` | NER with IOB/IOB2/BILUO tags, one token per line with columns separated by whitespace. The first column is the token and the final column is the NER tag. Sentences are separated by blank lines and documents are separated by the line `-DOCSTART- -X- O O`. Supports CoNLL 2003 NER format. See [sample data](%%GITHUB_SPACY/extra/example_data/ner_example_data). |
| `iob` | NER with IOB/IOB2/BILUO tags, one sentence per line with tokens separated by whitespace and annotation separated by `\|`, either `word\|B-ENT`or`word\|POS\|B-ENT`. See [sample data](%%GITHUB_SPACY/extra/example_data/ner_example_data). |
## debug {id="debug",version="3"}
The `spacy debug` CLI includes helpful commands for debugging and profiling your
configs, data and implementations.
### debug config {id="debug-config",version="3",tag="command"}
Debug a [`config.cfg` file](/usage/training#config) and show validation errors.
The command will create all objects in the tree and validate them. Note that
some config validation errors are blocking and will prevent the rest of the
config from being resolved. This means that you may not see all validation
errors at once and some issues are only shown once previous errors have been
fixed. To auto-fill a partial config and save the result, you can use the
[`init fill-config`](/api/cli#init-fill-config) command.
```bash
$ python -m spacy debug config [config_path] [--code] [--show-functions] [--show-variables] [overrides]
```
> #### Example
>
> ```bash
> $ python -m spacy debug config config.cfg
> ```
<Accordion title="Example output (validation error)">
```
✘ Config validation error
dropout field required
optimizer field required
optimize extra fields not permitted
{'seed': 0, 'accumulate_gradient': 1, 'dev_corpus': 'corpora.dev', 'train_corpus': 'corpora.train', 'gpu_allocator': None, 'patience': 1600, 'max_epochs': 0, 'max_steps': 20000, 'eval_frequency': 200, 'frozen_components': [], 'optimize': None, 'before_to_disk': None, 'batcher': {'@batchers': 'spacy.batch_by_words.v1', 'discard_oversize': False, 'tolerance': 0.2, 'get_length': None, 'size': {'@schedules': 'compounding.v1', 'start': 100, 'stop': 1000, 'compound': 1.001, 't': 0.0}}, 'logger': {'@loggers': 'spacy.ConsoleLogger.v1', 'progress_bar': False}, 'score_weights': {'tag_acc': 0.5, 'dep_uas': 0.25, 'dep_las': 0.25, 'sents_f': 0.0}}
If your config contains missing values, you can run the 'init fill-config'
command to fill in all the defaults, if possible:
python -m spacy init fill-config tmp/starter-config_invalid.cfg tmp/starter-config_invalid.cfg
```
</Accordion>
<Accordion title="Example output (valid config and all options)" spaced>
```bash
$ python -m spacy debug config ./config.cfg --show-functions --show-variables
```
```
============================= Config validation =============================
✔ Config is valid
=============================== Variables (6) ===============================
Variable Value
----------------------------------------- ----------------------------------
${components.tok2vec.model.encode.width} 96
${paths.dev} 'hello'
${paths.init_tok2vec} None
${paths.raw} None
${paths.train} ''
${system.seed} 0
========================= Registered functions (17) =========================
ℹ [nlp.tokenizer]
Registry @tokenizers
Name spacy.Tokenizer.v1
Module spacy.language
File /path/to/spacy/language.py (line 64)
ℹ [components.ner.model]
Registry @architectures
Name spacy.TransitionBasedParser.v3
Module spacy.ml.models.parser
File /path/to/spacy/ml/models/parser.py (line 11)
ℹ [components.ner.model.tok2vec]
Registry @architectures
Name spacy.Tok2VecListener.v1
Module spacy.ml.models.tok2vec
File /path/to/spacy/ml/models/tok2vec.py (line 16)
ℹ [components.parser.model]
Registry @architectures
Name spacy.TransitionBasedParser.v3
Module spacy.ml.models.parser
File /path/to/spacy/ml/models/parser.py (line 11)
ℹ [components.parser.model.tok2vec]
Registry @architectures
Name spacy.Tok2VecListener.v1
Module spacy.ml.models.tok2vec
File /path/to/spacy/ml/models/tok2vec.py (line 16)
ℹ [components.tagger.model]
Registry @architectures
Name spacy.Tagger.v1
Module spacy.ml.models.tagger
File /path/to/spacy/ml/models/tagger.py (line 9)
ℹ [components.tagger.model.tok2vec]
Registry @architectures
Name spacy.Tok2VecListener.v1
Module spacy.ml.models.tok2vec
File /path/to/spacy/ml/models/tok2vec.py (line 16)
ℹ [components.tok2vec.model]
Registry @architectures
Name spacy.Tok2Vec.v1
Module spacy.ml.models.tok2vec
File /path/to/spacy/ml/models/tok2vec.py (line 72)
ℹ [components.tok2vec.model.embed]
Registry @architectures
Name spacy.MultiHashEmbed.v1
Module spacy.ml.models.tok2vec
File /path/to/spacy/ml/models/tok2vec.py (line 93)
ℹ [components.tok2vec.model.encode]
Registry @architectures
Name spacy.MaxoutWindowEncoder.v1
Module spacy.ml.models.tok2vec
File /path/to/spacy/ml/models/tok2vec.py (line 207)
ℹ [corpora.dev]
Registry @readers
Name spacy.Corpus.v1
Module spacy.training.corpus
File /path/to/spacy/training/corpus.py (line 18)
ℹ [corpora.train]
Registry @readers
Name spacy.Corpus.v1
Module spacy.training.corpus
File /path/to/spacy/training/corpus.py (line 18)
ℹ [training.logger]
Registry @loggers
Name spacy.ConsoleLogger.v1
Module spacy.training.loggers
File /path/to/spacy/training/loggers.py (line 8)
ℹ [training.batcher]
Registry @batchers
Name spacy.batch_by_words.v1
Module spacy.training.batchers
File /path/to/spacy/training/batchers.py (line 49)
ℹ [training.batcher.size]
Registry @schedules
Name compounding.v1
Module thinc.schedules
File /path/to/thinc/thinc/schedules.py (line 43)
ℹ [training.optimizer]
Registry @optimizers
Name Adam.v1
Module thinc.optimizers
File /path/to/thinc/thinc/optimizers.py (line 58)
ℹ [training.optimizer.learn_rate]
Registry @schedules
Name warmup_linear.v1
Module thinc.schedules
File /path/to/thinc/thinc/schedules.py (line 91)
```
</Accordion>
| Name | Description |
| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. If `-`, the data will be [read from stdin](/usage/training#config-stdin). ~~Union[Path, str] \(positional)~~ |
| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--show-functions`, `-F` | Show an overview of all registered function blocks used in the config and where those functions come from, including the module name, Python file and line number. ~~bool (flag)~~ |
| `--show-variables`, `-V` | Show an overview of all variables referenced in the config, e.g. `${paths.train}` and their values that will be used. This also reflects any config overrides provided on the CLI, e.g. `--paths.train /path`. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ |
| **PRINTS** | Config validation errors, if available. |
### debug data {id="debug-data",tag="command"}
Analyze, debug and validate your training and development data. Get useful
stats, and find problems like invalid entity annotations, cyclic dependencies,
low data labels and more.
<Infobox title="New in v3.0" variant="warning">
The `debug data` command is now available as a subcommand of `spacy debug`. It
takes the same arguments as `train` and reads settings off the
[`config.cfg` file](/usage/training#config) and optional
[overrides](/usage/training#config-overrides) on the CLI.
</Infobox>
<Infobox title="Notes on span characteristics" emoji="💡">
If your pipeline contains a `spancat` component, then this command will also
report span characteristics such as the average span length and the span (or
span boundary) distinctiveness. The distinctiveness measure shows how different
the tokens are with respect to the rest of the corpus using the KL-divergence of
the token distributions. To learn more, you can check out Papay et al.'s work on
[_Dissecting Span Identification Tasks with Performance Prediction_ (EMNLP 2020)](https://aclanthology.org/2020.emnlp-main.396/).
</Infobox>
```bash
$ python -m spacy debug data [config_path] [--code] [--ignore-warnings] [--verbose] [--no-format] [overrides]
```
> #### Example
>
> ```bash
> $ python -m spacy debug data ./config.cfg
> ```
<Accordion title="Example output" spaced>
```
=========================== Data format validation ===========================
✔ Corpus is loadable
✔ Pipeline can be initialized with data
=============================== Training stats ===============================
Training pipeline: tagger, parser, ner
Starting with blank model 'en'
18127 training docs
2939 evaluation docs
⚠ 34 training examples also in evaluation data
============================== Vocab & Vectors ==============================
ℹ 2083156 total words in the data (56962 unique)
⚠ 13020 misaligned tokens in the training data
⚠ 2423 misaligned tokens in the dev data
10 most common words: 'the' (98429), ',' (91756), '.' (87073), 'to' (50058),
'of' (49559), 'and' (44416), 'a' (34010), 'in' (31424), 'that' (22792), 'is'
(18952)
ℹ No word vectors present in the model
========================== Named Entity Recognition ==========================
ℹ 18 new labels, 0 existing labels
528978 missing values (tokens with '-' label)
New: 'ORG' (23860), 'PERSON' (21395), 'GPE' (21193), 'DATE' (18080), 'CARDINAL'
(10490), 'NORP' (9033), 'MONEY' (5164), 'PERCENT' (3761), 'ORDINAL' (2122),
'LOC' (2113), 'TIME' (1616), 'WORK_OF_ART' (1229), 'QUANTITY' (1150), 'FAC'
(1134), 'EVENT' (974), 'PRODUCT' (935), 'LAW' (444), 'LANGUAGE' (338)
✔ Good amount of examples for all labels
✔ Examples without occurences available for all labels
✔ No entities consisting of or starting/ending with whitespace
=========================== Part-of-speech Tagging ===========================
ℹ 49 labels in data
'NN' (266331), 'IN' (227365), 'DT' (185600), 'NNP' (164404), 'JJ' (119830),
'NNS' (110957), '.' (101482), ',' (92476), 'RB' (90090), 'PRP' (90081), 'VB'
(74538), 'VBD' (68199), 'CC' (62862), 'VBZ' (50712), 'VBP' (43420), 'VBN'
(42193), 'CD' (40326), 'VBG' (34764), 'TO' (31085), 'MD' (25863), 'PRP$'
(23335), 'HYPH' (13833), 'POS' (13427), 'UH' (13322), 'WP' (10423), 'WDT'
(9850), 'RP' (8230), 'WRB' (8201), ':' (8168), '''' (7392), '``' (6984), 'NNPS'
(5817), 'JJR' (5689), '$' (3710), 'EX' (3465), 'JJS' (3118), 'RBR' (2872),
'-RRB-' (2825), '-LRB-' (2788), 'PDT' (2078), 'XX' (1316), 'RBS' (1142), 'FW'
(794), 'NFP' (557), 'SYM' (440), 'WP$' (294), 'LS' (293), 'ADD' (191), 'AFX'
(24)
============================= Dependency Parsing =============================
ℹ Found 111703 sentences with an average length of 18.6 words.
ℹ Found 2251 nonprojective train sentences
ℹ Found 303 nonprojective dev sentences
ℹ 47 labels in train data
ℹ 211 labels in projectivized train data
'punct' (236796), 'prep' (188853), 'pobj' (182533), 'det' (172674), 'nsubj'
(169481), 'compound' (116142), 'ROOT' (111697), 'amod' (107945), 'dobj' (93540),
'aux' (86802), 'advmod' (86197), 'cc' (62679), 'conj' (59575), 'poss' (36449),
'ccomp' (36343), 'advcl' (29017), 'mark' (27990), 'nummod' (24582), 'relcl'
(21359), 'xcomp' (21081), 'attr' (18347), 'npadvmod' (17740), 'acomp' (17204),
'auxpass' (15639), 'appos' (15368), 'neg' (15266), 'nsubjpass' (13922), 'case'
(13408), 'acl' (12574), 'pcomp' (10340), 'nmod' (9736), 'intj' (9285), 'prt'
(8196), 'quantmod' (7403), 'dep' (4300), 'dative' (4091), 'agent' (3908), 'expl'
(3456), 'parataxis' (3099), 'oprd' (2326), 'predet' (1946), 'csubj' (1494),
'subtok' (1147), 'preconj' (692), 'meta' (469), 'csubjpass' (64), 'iobj' (1)
⚠ Low number of examples for label 'iobj' (1)
⚠ Low number of examples for 130 labels in the projectivized dependency
trees used for training. You may want to projectivize labels such as punct
before training in order to improve parser performance.
⚠ Projectivized labels with low numbers of examples: appos||attr: 12
advmod||dobj: 13 prep||ccomp: 12 nsubjpass||ccomp: 15 pcomp||prep: 14
amod||dobj: 9 attr||xcomp: 14 nmod||nsubj: 17 prep||advcl: 2 prep||prep: 5
nsubj||conj: 12 advcl||advmod: 18 ccomp||advmod: 11 ccomp||pcomp: 5 acl||pobj:
10 npadvmod||acomp: 7 dobj||pcomp: 14 nsubjpass||pcomp: 1 nmod||pobj: 8
amod||attr: 6 nmod||dobj: 12 aux||conj: 1 neg||conj: 1 dative||xcomp: 11
pobj||dative: 3 xcomp||acomp: 19 advcl||pobj: 2 nsubj||advcl: 2 csubj||ccomp: 1
advcl||acl: 1 relcl||nmod: 2 dobj||advcl: 10 advmod||advcl: 3 nmod||nsubjpass: 6
amod||pobj: 5 cc||neg: 1 attr||ccomp: 16 advcl||xcomp: 3 nmod||attr: 4
advcl||nsubjpass: 5 advcl||ccomp: 4 ccomp||conj: 1 punct||acl: 1 meta||acl: 1
parataxis||acl: 1 prep||acl: 1 amod||nsubj: 7 ccomp||ccomp: 3 acomp||xcomp: 5
dobj||acl: 5 prep||oprd: 6 advmod||acl: 2 dative||advcl: 1 pobj||agent: 5
xcomp||amod: 1 dep||advcl: 1 prep||amod: 8 relcl||compound: 1 advcl||csubj: 3
npadvmod||conj: 2 npadvmod||xcomp: 4 advmod||nsubj: 3 ccomp||amod: 7
advcl||conj: 1 nmod||conj: 2 advmod||nsubjpass: 2 dep||xcomp: 2 appos||ccomp: 1
advmod||dep: 1 advmod||advmod: 5 aux||xcomp: 8 dep||advmod: 1 dative||ccomp: 2
prep||dep: 1 conj||conj: 1 dep||ccomp: 4 cc||ROOT: 1 prep||ROOT: 1 nsubj||pcomp:
3 advmod||prep: 2 relcl||dative: 1 acl||conj: 1 advcl||attr: 4 prep||npadvmod: 1
nsubjpass||xcomp: 1 neg||advmod: 1 xcomp||oprd: 1 advcl||advcl: 1 dobj||dep: 3
nsubjpass||parataxis: 1 attr||pcomp: 1 ccomp||parataxis: 1 advmod||attr: 1
nmod||oprd: 1 appos||nmod: 2 advmod||relcl: 1 appos||npadvmod: 1 appos||conj: 1
prep||expl: 1 nsubjpass||conj: 1 punct||pobj: 1 cc||pobj: 1 conj||pobj: 1
punct||conj: 1 ccomp||dep: 1 oprd||xcomp: 3 ccomp||xcomp: 1 ccomp||nsubj: 1
nmod||dep: 1 xcomp||ccomp: 1 acomp||advcl: 1 intj||advmod: 1 advmod||acomp: 2
relcl||oprd: 1 advmod||prt: 1 advmod||pobj: 1 appos||nummod: 1 relcl||npadvmod:
3 mark||advcl: 1 aux||ccomp: 1 amod||nsubjpass: 1 npadvmod||advmod: 1 conj||dep:
1 nummod||pobj: 1 amod||npadvmod: 1 intj||pobj: 1 nummod||npadvmod: 1
xcomp||xcomp: 1 aux||dep: 1 advcl||relcl: 1
⚠ The following labels were found only in the train data: xcomp||amod,
advcl||relcl, prep||nsubjpass, acl||nsubj, nsubjpass||conj, xcomp||oprd,
advmod||conj, advmod||advmod, iobj, advmod||nsubjpass, dobj||conj, ccomp||amod,
meta||acl, xcomp||xcomp, prep||attr, prep||ccomp, advcl||acomp, acl||dobj,
advcl||advcl, pobj||agent, prep||advcl, nsubjpass||xcomp, prep||dep,
acomp||xcomp, aux||ccomp, ccomp||dep, conj||dep, relcl||compound,
nsubjpass||ccomp, nmod||dobj, advmod||advcl, advmod||acl, dobj||advcl,
dative||xcomp, prep||nsubj, ccomp||ccomp, nsubj||ccomp, xcomp||acomp,
prep||acomp, dep||advmod, acl||pobj, appos||dobj, npadvmod||acomp, cc||ROOT,
relcl||nsubj, nmod||pobj, acl||nsubjpass, ccomp||advmod, pcomp||prep,
amod||dobj, advmod||attr, advcl||csubj, appos||attr, dobj||pcomp, prep||ROOT,
relcl||pobj, advmod||pobj, amod||nsubj, ccomp||xcomp, prep||oprd,
npadvmod||advmod, appos||nummod, advcl||pobj, neg||advmod, acl||attr,
appos||nsubjpass, csubj||ccomp, amod||nsubjpass, intj||pobj, dep||advcl,
cc||neg, xcomp||ccomp, dative||ccomp, nmod||oprd, pobj||dative, prep||dobj,
dep||ccomp, relcl||attr, ccomp||nsubj, advcl||xcomp, nmod||dep, advcl||advmod,
ccomp||conj, pobj||prep, advmod||acomp, advmod||relcl, attr||pcomp,
ccomp||parataxis, oprd||xcomp, intj||advmod, nmod||nsubjpass, prep||npadvmod,
parataxis||acl, prep||pobj, advcl||dobj, amod||pobj, prep||acl, conj||pobj,
advmod||dep, punct||pobj, ccomp||acomp, acomp||advcl, nummod||npadvmod,
dobj||dep, npadvmod||xcomp, advcl||conj, relcl||npadvmod, punct||acl,
relcl||dobj, dobj||xcomp, nsubjpass||parataxis, dative||advcl, relcl||nmod,
advcl||ccomp, appos||npadvmod, ccomp||pcomp, prep||amod, mark||advcl,
prep||advmod, prep||xcomp, appos||nsubj, attr||ccomp, advmod||prt, dobj||ccomp,
aux||conj, advcl||nsubj, conj||conj, advmod||ccomp, advcl||nsubjpass,
attr||xcomp, nmod||conj, npadvmod||conj, relcl||dative, prep||expl,
nsubjpass||pcomp, advmod||xcomp, advmod||dobj, appos||pobj, nsubj||conj,
relcl||nsubjpass, advcl||attr, appos||ccomp, advmod||prep, prep||conj,
nmod||attr, punct||conj, neg||conj, dep||xcomp, aux||xcomp, dobj||acl,
nummod||pobj, amod||npadvmod, nsubj||pcomp, advcl||acl, appos||nmod,
relcl||oprd, prep||prep, cc||pobj, nmod||nsubj, amod||attr, aux||dep,
appos||conj, advmod||nsubj, nsubj||advcl, acl||conj
To train a parser, your data should include at least 20 instances of each label.
⚠ Multiple root labels (ROOT, nsubj, aux, npadvmod, prep) found in
training data. spaCy's parser uses a single root label ROOT so this distinction
will not be available.
================================== Summary ==================================
✔ 5 checks passed
⚠ 8 warnings
```
</Accordion>
| Name | Description |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. If `-`, the data will be [read from stdin](/usage/training#config-stdin). ~~Union[Path, str] \(positional)~~ |
| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--ignore-warnings`, `-IW` | Ignore warnings, only show stats and errors. ~~bool (flag)~~ |
| `--verbose`, `-V` | Print additional information and explanations. ~~bool (flag)~~ |
| `--no-format`, `-NF` | Don't pretty-print the results. Use this if you want to write to a file. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ |
| **PRINTS** | Debugging information. |
### debug diff-config {id="debug-diff",tag="command"}
Show a diff of a config file with respect to spaCy's defaults or another config
file. If additional settings were used in the creation of the config file, then
you must supply these as extra parameters to the command when comparing to the
default settings. The generated diff can also be used when posting to the
discussion forum to provide more information for the maintainers.
```bash
$ python -m spacy debug diff-config [config_path] [--compare-to] [--optimize] [--gpu] [--pretraining] [--markdown]
```
> #### Example
>
> ```bash
> $ python -m spacy debug diff-config ./config.cfg
> ```
<Accordion title="Example output" spaced>
```
ℹ Found user-defined language: 'en'
ℹ Found user-defined pipelines: ['tok2vec', 'tagger', 'parser',
'ner']
[paths]
+ train = "./data/train.spacy"
+ dev = "./data/dev.spacy"
- train = null
- dev = null
vectors = null
init_tok2vec = null
[system]
gpu_allocator = null
+ seed = 42
- seed = 0
[nlp]
lang = "en"
pipeline = ["tok2vec","tagger","parser","ner"]
batch_size = 1000
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
[components]
[components.ner]
factory = "ner"
incorrect_spans_key = null
moves = null
scorer = {"@scorers":"spacy.ner_scorer.v1"}
update_with_oracle_cut_size = 100
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v3"
state_type = "ner"
extra_state_tokens = false
- hidden_width = 64
+ hidden_width = 36
maxout_pieces = 2
use_upper = true
nO = null
[components.ner.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
upstream = "*"
[components.parser]
factory = "parser"
learn_tokens = false
min_action_freq = 30
moves = null
scorer = {"@scorers":"spacy.parser_scorer.v1"}
update_with_oracle_cut_size = 100
[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v3"
state_type = "parser"
extra_state_tokens = false
hidden_width = 128
maxout_pieces = 3
use_upper = true
nO = null
[components.parser.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
upstream = "*"
[components.tagger]
factory = "tagger"
neg_prefix = "!"
overwrite = false
scorer = {"@scorers":"spacy.tagger_scorer.v1"}
[components.tagger.model]
@architectures = "spacy.Tagger.v1"
nO = null
[components.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
upstream = "*"
[components.tok2vec]
factory = "tok2vec"
[components.tok2vec.model]
@architectures = "spacy.Tok2Vec.v2"
[components.tok2vec.model.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = ${components.tok2vec.model.encode.width}
attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
rows = [5000,2500,2500,2500]
include_static_vectors = false
[components.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = 96
depth = 4
window_size = 1
maxout_pieces = 3
[corpora]
[corpora.dev]
@readers = "spacy.Corpus.v1"
path = ${paths.dev}
max_length = 0
gold_preproc = false
limit = 0
augmenter = null
[corpora.train]
@readers = "spacy.Corpus.v1"
path = ${paths.train}
max_length = 0
gold_preproc = false
limit = 0
augmenter = null
[training]
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
seed = ${system.seed}
gpu_allocator = ${system.gpu_allocator}
dropout = 0.1
accumulate_gradient = 1
patience = 1600
max_epochs = 0
max_steps = 20000
eval_frequency = 200
frozen_components = []
annotating_components = []
before_to_disk = null
[training.batcher]
@batchers = "spacy.batch_by_words.v1"
discard_oversize = false
tolerance = 0.2
get_length = null
[training.batcher.size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001
t = 0.0
[training.logger]
@loggers = "spacy.ConsoleLogger.v1"
progress_bar = false
[training.optimizer]
@optimizers = "Adam.v1"
beta1 = 0.9
beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
use_averages = false
eps = 0.00000001
learn_rate = 0.001
[training.score_weights]
tag_acc = 0.33
dep_uas = 0.17
dep_las = 0.17
dep_las_per_type = null
sents_p = null
sents_r = null
sents_f = 0.0
ents_f = 0.33
ents_p = 0.0
ents_r = 0.0
ents_per_type = null
[pretraining]
[initialize]
vectors = ${paths.vectors}
init_tok2vec = ${paths.init_tok2vec}
vocab_data = null
lookups = null
before_init = null
after_init = null
[initialize.components]
[initialize.tokenizer]
```
</Accordion>
| Name | Description |
| -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. ~~Union[Path, str] \(positional)~~ |
| `compare_to` | Path to another config file to diff against, or `None` to compare against default settings. ~~Optional[Union[Path, str] \(option)~~ |
| `optimize`, `-o` | `"efficiency"` or `"accuracy"`. Whether the config was optimized for efficiency (faster inference, smaller model, lower memory consumption) or higher accuracy (potentially larger and slower model). Only relevant when comparing against a default config. Defaults to `"efficiency"`. ~~str (option)~~ |
| `gpu`, `-G` | Whether the config was made to run on a GPU. Only relevant when comparing against a default config. ~~bool (flag)~~ |
| `pretraining`, `-pt` | Include config for pretraining (with [`spacy pretrain`](/api/cli#pretrain)). Only relevant when comparing against a default config. Defaults to `False`. ~~bool (flag)~~ |
| `markdown`, `-md` | Generate Markdown for Github issues. Defaults to `False`. ~~bool (flag)~~ |
| **PRINTS** | Diff between the two config files. |
### debug profile {id="debug-profile",tag="command"}
Profile which functions take the most time in a spaCy pipeline. Input should be
formatted as one JSON object per line with a key `"text"`. It can either be
provided as a JSONL file, or be read from `sys.sytdin`. If no input file is
specified, the IMDB dataset is loaded via
[`ml_datasets`](https://github.com/explosion/ml_datasets).
<Infobox title="New in v3.0" variant="warning">
The `profile` command is now available as a subcommand of `spacy debug`.
</Infobox>
```bash
$ python -m spacy debug profile [model] [inputs] [--n-texts]
```
| Name | Description |
| ----------------- | ---------------------------------------------------------------------------------- |
| `model` | A loadable spaCy pipeline (package name or path). ~~str (positional)~~ |
| `inputs` | Path to input file, or `-` for standard input. ~~Path (positional)~~ |
| `--n-texts`, `-n` | Maximum number of texts to use if available. Defaults to `10000`. ~~int (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **PRINTS** | Profiling information for the pipeline. |
### debug model {id="debug-model",version="3",tag="command"}
Debug a Thinc [`Model`](https://thinc.ai/docs/api-model) by running it on a
sample text and checking how it updates its internal weights and parameters.
```bash
$ python -m spacy debug model [config_path] [component] [--layers] [--dimensions] [--parameters] [--gradients] [--attributes] [--print-step0] [--print-step1] [--print-step2] [--print-step3] [--gpu-id]
```
<Accordion title="Example outputs" spaced>
In this example log, we just print the name of each layer after creation of the
model ("Step 0"), which helps us to understand the internal structure of the
Neural Network, and to focus on specific layers that we want to inspect further
(see next example).
```bash
$ python -m spacy debug model ./config.cfg tagger -P0
```
```
ℹ Using CPU
ℹ Fixing random seed: 0
ℹ Analysing model with ID 62
========================== STEP 0 - before training ==========================
ℹ Layer 0: model ID 62:
'extract_features>>list2ragged>>with_array-ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed>>with_array-maxout>>layernorm>>dropout>>ragged2list>>with_array-residual>>residual>>residual>>residual>>with_array-softmax'
ℹ Layer 1: model ID 59:
'extract_features>>list2ragged>>with_array-ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed>>with_array-maxout>>layernorm>>dropout>>ragged2list>>with_array-residual>>residual>>residual>>residual'
ℹ Layer 2: model ID 61: 'with_array-softmax'
ℹ Layer 3: model ID 24:
'extract_features>>list2ragged>>with_array-ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed>>with_array-maxout>>layernorm>>dropout>>ragged2list'
ℹ Layer 4: model ID 58: 'with_array-residual>>residual>>residual>>residual'
ℹ Layer 5: model ID 60: 'softmax'
ℹ Layer 6: model ID 13: 'extract_features'
ℹ Layer 7: model ID 14: 'list2ragged'
ℹ Layer 8: model ID 16:
'with_array-ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed'
ℹ Layer 9: model ID 22: 'with_array-maxout>>layernorm>>dropout'
ℹ Layer 10: model ID 23: 'ragged2list'
ℹ Layer 11: model ID 57: 'residual>>residual>>residual>>residual'
ℹ Layer 12: model ID 15:
'ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed'
ℹ Layer 13: model ID 21: 'maxout>>layernorm>>dropout'
ℹ Layer 14: model ID 32: 'residual'
ℹ Layer 15: model ID 40: 'residual'
ℹ Layer 16: model ID 48: 'residual'
ℹ Layer 17: model ID 56: 'residual'
ℹ Layer 18: model ID 3: 'ints-getitem>>hashembed'
ℹ Layer 19: model ID 6: 'ints-getitem>>hashembed'
ℹ Layer 20: model ID 9: 'ints-getitem>>hashembed'
...
```
In this example log, we see how initialization of the model (Step 1) propagates
the correct values for the `nI` (input) and `nO` (output) dimensions of the
various layers. In the `softmax` layer, this step also defines the `W` matrix as
an all-zero matrix determined by the `nO` and `nI` dimensions. After a first
training step (Step 2), this matrix has clearly updated its values through the
training feedback loop.
```bash
$ python -m spacy debug model ./config.cfg tagger -l "5,15" -DIM -PAR -P0 -P1 -P2
```
```
ℹ Using CPU
ℹ Fixing random seed: 0
ℹ Analysing model with ID 62
========================= STEP 0 - before training =========================
ℹ Layer 5: model ID 60: 'softmax'
ℹ - dim nO: None
ℹ - dim nI: 96
ℹ - param W: None
ℹ - param b: None
ℹ Layer 15: model ID 40: 'residual'
ℹ - dim nO: None
ℹ - dim nI: None
======================= STEP 1 - after initialization =======================
ℹ Layer 5: model ID 60: 'softmax'
ℹ - dim nO: 4
ℹ - dim nI: 96
ℹ - param W: (4, 96) - sample: [0. 0. 0. 0. 0.]
ℹ - param b: (4,) - sample: [0. 0. 0. 0.]
ℹ Layer 15: model ID 40: 'residual'
ℹ - dim nO: 96
ℹ - dim nI: None
========================== STEP 2 - after training ==========================
ℹ Layer 5: model ID 60: 'softmax'
ℹ - dim nO: 4
ℹ - dim nI: 96
ℹ - param W: (4, 96) - sample: [ 0.00283958 -0.00294119 0.00268396 -0.00296219
-0.00297141]
ℹ - param b: (4,) - sample: [0.00300002 0.00300002 0.00300002 0.00300002]
ℹ Layer 15: model ID 40: 'residual'
ℹ - dim nO: 96
ℹ - dim nI: None
```
</Accordion>
| Name | Description |
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. If `-`, the data will be [read from stdin](/usage/training#config-stdin). ~~Union[Path, str] \(positional)~~ |
| `component` | Name of the pipeline component of which the model should be analyzed. ~~str (positional)~~ |
| `--layers`, `-l` | Comma-separated names of layer IDs to print. ~~str (option)~~ |
| `--dimensions`, `-DIM` | Show dimensions of each layer. ~~bool (flag)~~ |
| `--parameters`, `-PAR` | Show parameters of each layer. ~~bool (flag)~~ |
| `--gradients`, `-GRAD` | Show gradients of each layer. ~~bool (flag)~~ |
| `--attributes`, `-ATTR` | Show attributes of each layer. ~~bool (flag)~~ |
| `--print-step0`, `-P0` | Print model before training. ~~bool (flag)~~ |
| `--print-step1`, `-P1` | Print model after initialization. ~~bool (flag)~~ |
| `--print-step2`, `-P2` | Print model after training. ~~bool (flag)~~ |
| `--print-step3`, `-P3` | Print final predictions. ~~bool (flag)~~ |
| `--gpu-id`, `-g` | GPU ID or `-1` for CPU. Defaults to `-1`. ~~int (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ |
| **PRINTS** | Debugging information. |
## train {id="train",tag="command"}
Train a pipeline. Expects data in spaCy's
[binary format](/api/data-formats#training) and a
[config file](/api/data-formats#config) with all settings and hyperparameters.
Will save out the best model from all epochs, as well as the final pipeline. The
`--code` argument can be used to provide a Python file that's imported before
the training process starts. This lets you register
[custom functions](/usage/training#custom-functions) and architectures and refer
to them in your config, all while still using spaCy's built-in `train` workflow.
If you need to manage complex multi-step training workflows, check out the new
[spaCy projects](/usage/projects).
<Infobox title="New in v3.0" variant="warning">
The `train` command doesn't take a long list of command-line arguments anymore
and instead expects a single [`config.cfg` file](/usage/training#config)
containing all settings for the pipeline, training process and hyperparameters.
Config values can be [overwritten](/usage/training#config-overrides) on the CLI
if needed. For example, `--paths.train ./train.spacy` sets the variable `train`
in the section `[paths]`.
</Infobox>
> #### Example
>
> ```bash
> $ python -m spacy train config.cfg --output ./output --paths.train ./train --paths.dev ./dev
> ```
```bash
$ python -m spacy train [config_path] [--output] [--code] [--verbose] [--gpu-id] [overrides]
```
| Name | Description |
| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. If `-`, the data will be [read from stdin](/usage/training#config-stdin). ~~Union[Path, str] \(positional)~~ |
| `--output`, `-o` | Directory to store trained pipeline in. Will be created if it doesn't exist. ~~Optional[Path] \(option)~~ |
| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--verbose`, `-V` | Show more detailed messages during training. ~~bool (flag)~~ |
| `--gpu-id`, `-g` | GPU ID or `-1` for CPU. Defaults to `-1`. ~~int (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ |
| **CREATES** | The final trained pipeline and the best trained pipeline. |
### Calling the training function from Python {id="train-function",version="3.2"}
The training CLI exposes a `train` helper function that lets you run the
training just like `spacy train`. Usually it's easier to use the command line
directly, but if you need to kick off training from code this is how to do it.
> #### Example
>
> ```python
> from spacy.cli.train import train
>
> train("./config.cfg", overrides={"paths.train": "./train.spacy", "paths.dev": "./dev.spacy"})
>
> ```
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| `config_path` | Path to the config to use for training. ~~Union[str, Path]~~ |
| `output_path` | Optional name of directory to save output model in. If not provided a model will not be saved. ~~Optional[Union[str, Path]]~~ |
| _keyword-only_ | |
| `use_gpu` | Which GPU to use. Defaults to -1 for no GPU. ~~int~~ |
| `overrides` | Values to override config settings. ~~Dict[str, Any]~~ |
## pretrain {id="pretrain",version="2.1",tag="command,experimental"}
Pretrain the "token to vector" ([`Tok2vec`](/api/tok2vec)) layer of pipeline
components on raw text, using an approximate language-modeling objective.
Specifically, we load pretrained vectors, and train a component like a CNN,
BiLSTM, etc to predict vectors which match the pretrained ones. The weights are
saved to a directory after each epoch. You can then include a **path to one of
these pretrained weights files** in your
[training config](/usage/training#config) as the `init_tok2vec` setting when you
train your pipeline. This technique may be especially helpful if you have little
labelled data. See the usage docs on
[pretraining](/usage/embeddings-transformers#pretraining) for more info. To read
the raw text, a [`JsonlCorpus`](/api/top-level#jsonlcorpus) is typically used.
<Infobox title="Changed in v3.0" variant="warning">
As of spaCy v3.0, the `pretrain` command takes the same
[config file](/usage/training#config) as the `train` command. This ensures that
settings are consistent between pretraining and training. Settings for
pretraining can be defined in the `[pretraining]` block of the config file and
auto-generated by setting `--pretraining` on
[`init fill-config`](/api/cli#init-fill-config). Also see the
[data format](/api/data-formats#config) for details.
</Infobox>
> #### Example
>
> ```bash
> $ python -m spacy pretrain config.cfg ./output_pretrain --paths.raw_text ./data.jsonl
> ```
```bash
$ python -m spacy pretrain [config_path] [output_dir] [--code] [--resume-path] [--epoch-resume] [--gpu-id] [overrides]
```
| Name | Description |
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. If `-`, the data will be [read from stdin](/usage/training#config-stdin). ~~Union[Path, str] \(positional)~~ |
| `output_dir` | Directory to save binary weights to on each epoch. ~~Path (positional)~~ |
| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--resume-path`, `-r` | Path to pretrained weights from which to resume pretraining. ~~Optional[Path] \(option)~~ |
| `--epoch-resume`, `-er` | The epoch to resume counting from when using `--resume-path`. Prevents unintended overwriting of existing weight files. ~~Optional[int] \(option)~~ |
| `--gpu-id`, `-g` | GPU ID or `-1` for CPU. Defaults to `-1`. ~~int (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--training.dropout 0.2`. ~~Any (option/flag)~~ |
| **CREATES** | The pretrained weights that can be used to initialize `spacy train`. |
## evaluate {id="evaluate",version="2",tag="command"}
Evaluate a trained pipeline. Expects a loadable spaCy pipeline (package name or
path) and evaluation data in the
[binary `.spacy` format](/api/data-formats#binary-training). The
`--gold-preproc` option sets up the evaluation examples with gold-standard
sentences and tokens for the predictions. Gold preprocessing helps the
annotations align to the tokenization, and may result in sequences of more
consistent length. However, it may reduce runtime accuracy due to train/test
skew. To render a sample of dependency parses in a HTML file using the
[displaCy visualizations](/usage/visualizers), set as output directory as the
`--displacy-path` argument.
```bash
$ python -m spacy evaluate [model] [data_path] [--output] [--code] [--gold-preproc] [--gpu-id] [--displacy-path] [--displacy-limit]
```
| Name | Description |
| ----------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `model` | Pipeline to evaluate. Can be a package or a path to a data directory. ~~str (positional)~~ |
| `data_path` | Location of evaluation data in spaCy's [binary format](/api/data-formats#training). ~~Path (positional)~~ |
| `--output`, `-o` | Output JSON file for metrics. If not set, no metrics will be exported. ~~Optional[Path] \(option)~~ |
| `--code`, `-c` <Tag variant="new">3</Tag> | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--gold-preproc`, `-G` | Use gold preprocessing. ~~bool (flag)~~ |
| `--gpu-id`, `-g` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ |
| `--displacy-path`, `-dp` | Directory to output rendered parses as HTML. If not set, no visualizations will be generated. ~~Optional[Path] \(option)~~ |
| `--displacy-limit`, `-dl` | Number of parses to generate per file. Defaults to `25`. Keep in mind that a significantly higher number might cause the `.html` files to render slowly. ~~int (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | Training results and optional metrics and visualizations. |
## apply {id="apply", version="3.5", tag="command"}
Applies a trained pipeline to data and stores the resulting annotated documents
in a `DocBin`. The input can be a single file or a directory. The recognized
input formats are:
1. `.spacy`
2. `.jsonl` containing a user specified `text_key`
3. Files with any other extension are assumed to be plain text files containing
a single document.
When a directory is provided it is traversed recursively to collect all files.
```cli
$ python -m spacy apply [model] [data-path] [output-file] [--code] [--text-key] [--force-overwrite] [--gpu-id] [--batch-size] [--n-process]
```
| Name | Description |
| ----------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `model` | Pipeline to apply to the data. Can be a package or a path to a data directory. ~~str (positional)~~ |
| `data_path` | Location of data to be evaluated in spaCy's [binary format](/api/data-formats#training), jsonl, or plain text. ~~Path (positional)~~ |
| `output-file`, `-o` | Output `DocBin` path. ~~str (positional)~~ |
| `--code`, `-c` <Tag variant="new">3</Tag> | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--text-key`, `-tk` | The key for `.jsonl` files to use to grab the texts from. Defaults to `text`. ~~Optional[str] \(option)~~ |
| `--force-overwrite`, `-F` | If the provided `output-file` already exists, then force `apply` to overwrite it. If this is `False` (default) then quits with a warning instead. ~~bool (flag)~~ |
| `--gpu-id`, `-g` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ |
| `--batch-size`, `-b` | Batch size to use for prediction. Defaults to `1`. ~~int (option)~~ |
| `--n-process`, `-n` | Number of processes to use for prediction. Defaults to `1`. ~~int (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | A `DocBin` with the annotations from the `model` for all the files found in `data-path`. |
## find-threshold {id="find-threshold",version="3.5",tag="command"}
Runs prediction trials for a trained model with varying tresholds to maximize
the specified metric. The search space for the threshold is traversed linearly
from 0 to 1 in `n_trials` steps. Results are displayed in a table on `stdout`
(the corresponding API call to `spacy.cli.find_threshold.find_threshold()`
returns all results).
This is applicable only for components whose predictions are influenced by
thresholds - e.g. `textcat_multilabel` and `spancat`, but not `textcat`. Note
that the full path to the corresponding threshold attribute in the config has to
be provided.
> #### Examples
>
> ```bash
> # For textcat_multilabel:
> $ python -m spacy find-threshold my_nlp data.spacy textcat_multilabel threshold cats_macro_f
> ```
>
> ```bash
> # For spancat:
> $ python -m spacy find-threshold my_nlp data.spacy spancat threshold spans_sc_f
> ```
| Name | Description |
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `model` | Pipeline to evaluate. Can be a package or a path to a data directory. ~~str (positional)~~ |
| `data_path` | Path to file with DocBin with docs to use for threshold search. ~~Path (positional)~~ |
| `pipe_name` | Name of pipe to examine thresholds for. ~~str (positional)~~ |
| `threshold_key` | Key of threshold attribute in component's configuration. ~~str (positional)~~ |
| `scores_key` | Name of score to metric to optimize. ~~str (positional)~~ |
| `--n_trials`, `-n` | Number of trials to determine optimal thresholds. ~~int (option)~~ |
| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--gpu-id`, `-g` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ |
| `--gold-preproc`, `-G` | Use gold preprocessing. ~~bool (flag)~~ |
| `--silent`, `-V`, `-VV` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
## assemble {id="assemble",tag="command"}
Assemble a pipeline from a config file without additional training. Expects a
[config file](/api/data-formats#config) with all settings and hyperparameters.
The `--code` argument can be used to import a Python file that lets you register
[custom functions](/usage/training#custom-functions) and refer to them in your
config.
> #### Example
>
> ```bash
> $ python -m spacy assemble config.cfg ./output
> ```
```bash
$ python -m spacy assemble [config_path] [output_dir] [--code] [--verbose] [overrides]
```
| Name | Description |
| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `config_path` | Path to the [config](/api/data-formats#config) file containing all settings and hyperparameters. If `-`, the data will be [read from stdin](/usage/training#config-stdin). ~~Union[Path, str] \(positional)~~ |
| `output_dir` | Directory to store the final pipeline in. Will be created if it doesn't exist. ~~Optional[Path] \(option)~~ |
| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions). ~~Optional[Path] \(option)~~ |
| `--verbose`, `-V` | Show more detailed messages during processing. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.data ./data`. ~~Any (option/flag)~~ |
| **CREATES** | The final assembled pipeline. |
## package {id="package",tag="command"}
Generate an installable [Python package](/usage/training#models-generating) from
an existing pipeline data directory. All data files are copied over. If
additional code files are provided (e.g. Python files containing custom
registered functions like
[pipeline components](/usage/processing-pipelines#custom-components)), they are
copied into the package and imported in the `__init__.py`. If the path to a
[`meta.json`](/api/data-formats#meta) is supplied, or a `meta.json` is found in
the input directory, this file is used. Otherwise, the data can be entered
directly from the command line. spaCy will then create a build artifact that you
can distribute and install with `pip install`. As of v3.1, the `package` command
will also create a formatted `README.md` based on the pipeline information
defined in the `meta.json`. If a `README.md` is already present in the source
directory, it will be used instead.
<Infobox title="New in v3.0" variant="warning">
The `spacy package` command now also builds the `.tar.gz` archive automatically,
so you don't have to run `python setup.py sdist` separately anymore. To disable
this, you can set `--build none`. You can also choose to build a binary wheel
(which installs more efficiently) by setting `--build wheel`, or to build both
the sdist and wheel by setting `--build sdist,wheel`.
</Infobox>
```bash
$ python -m spacy package [input_dir] [output_dir] [--code] [--meta-path] [--create-meta] [--build] [--name] [--version] [--force]
```
> #### Example
>
> ```bash
> $ python -m spacy package /input /output
> $ cd /output/en_pipeline-0.0.0
> $ pip install dist/en_pipeline-0.0.0.tar.gz
> ```
| Name | Description |
| -------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `input_dir` | Path to directory containing pipeline data. ~~Path (positional)~~ |
| `output_dir` | Directory to create package folder in. ~~Path (positional)~~ |
| `--code`, `-c` <Tag variant="new">3</Tag> | Comma-separated paths to Python files to be included in the package and imported in its `__init__.py`. This allows including [registering functions](/usage/training#custom-functions) and [custom components](/usage/processing-pipelines#custom-components). ~~str (option)~~ |
| `--meta-path`, `-m` | Path to [`meta.json`](/api/data-formats#meta) file (optional). ~~Optional[Path] \(option)~~ |
| `--create-meta`, `-C` | Create a `meta.json` file on the command line, even if one already exists in the directory. If an existing file is found, its entries will be shown as the defaults in the command line prompt. ~~bool (flag)~~ |
| `--build`, `-b` <Tag variant="new">3</Tag> | Comma-separated artifact formats to build. Can be `sdist` (for a `.tar.gz` archive) and/or `wheel` (for a binary `.whl` file), or `none` if you want to run this step manually. The generated artifacts can be installed by `pip install`. Defaults to `sdist`. ~~str (option)~~ |
| `--name`, `-n` <Tag variant="new">3</Tag> | Package name to override in meta. ~~Optional[str] \(option)~~ |
| `--version`, `-v` <Tag variant="new">3</Tag> | Package version to override in meta. Useful when training new versions, as it doesn't require editing the meta template. ~~Optional[str] \(option)~~ |
| `--force`, `-f` | Force overwriting of existing folder in output directory. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | A Python package containing the spaCy pipeline. |
## project {id="project",version="3"}
The `spacy project` CLI includes subcommands for working with
[spaCy projects](/usage/projects), end-to-end workflows for building and
deploying custom spaCy pipelines.
### project clone {id="project-clone",tag="command"}
Clone a project template from a Git repository. Calls into `git` under the hood
and can use the sparse checkout feature if available, so you're only downloading
what you need. By default, spaCy's
[project templates repo](https://github.com/explosion/projects) is used, but you
can provide any other repo (public or private) that you have access to using the
`--repo` option.
```bash
$ python -m spacy project clone [name] [dest] [--repo] [--branch] [--sparse]
```
> #### Example
>
> ```bash
> $ python -m spacy project clone pipelines/ner_wikiner
> ```
>
> Clone from custom repo:
>
> ```bash
> $ python -m spacy project clone template --repo https://github.com/your_org/your_repo
> ```
| Name | Description |
| ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | The name of the template to clone, relative to the repo. Can be a top-level directory or a subdirectory like `dir/template`. ~~str (positional)~~ |
| `dest` | Where to clone the project. Defaults to current working directory. ~~Path (positional)~~ |
| `--repo`, `-r` | The repository to clone from. Can be any public or private Git repo you have access to. ~~str (option)~~ |
| `--branch`, `-b` | The branch to clone from. Defaults to `master`. ~~str (option)~~ |
| `--sparse`, `-S` | Enable [sparse checkout](https://git-scm.com/docs/git-sparse-checkout) to only check out and download what's needed. Requires Git v22.2+. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | The cloned [project directory](/usage/projects#project-files). |
### project assets {id="project-assets",tag="command"}
Fetch project assets like datasets and pretrained weights. Assets are defined in
the `assets` section of the [`project.yml`](/usage/projects#project-yml). If a
`checksum` is provided, the file is only downloaded if no local file with the
same checksum exists and spaCy will show an error if the checksum of the
downloaded file doesn't match. If assets don't specify a `url` they're
considered "private" and you have to take care of putting them into the
destination directory yourself. If a local path is provided, the asset is copied
into the current project.
```bash
$ python -m spacy project assets [project_dir]
```
> #### Example
>
> ```bash
> $ python -m spacy project assets [--sparse]
> ```
| Name | Description |
| ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `project_dir` | Path to project directory. Defaults to current working directory. ~~Path (positional)~~ |
| `--sparse`, `-S` | Enable [sparse checkout](https://git-scm.com/docs/git-sparse-checkout) to only check out and download what's needed. Requires Git v22.2+. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | Downloaded or copied assets defined in the `project.yml`. |
### project run {id="project-run",tag="command"}
Run a named command or workflow defined in the
[`project.yml`](/usage/projects#project-yml). If a workflow name is specified,
all commands in the workflow are run, in order. If commands define
[dependencies or outputs](/usage/projects#deps-outputs), they will only be
re-run if state has changed. For example, if the input dataset changes, a
preprocessing command that depends on those files will be re-run.
```bash
$ python -m spacy project run [subcommand] [project_dir] [--force] [--dry]
```
> #### Example
>
> ```bash
> $ python -m spacy project run train
> ```
| Name | Description |
| --------------- | --------------------------------------------------------------------------------------- |
| `subcommand` | Name of the command or workflow to run. ~~str (positional)~~ |
| `project_dir` | Path to project directory. Defaults to current working directory. ~~Path (positional)~~ |
| `--force`, `-F` | Force re-running steps, even if nothing changed. ~~bool (flag)~~ |
| `--dry`, `-D` | Perform a dry run and don't execute scripts. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **EXECUTES** | The command defined in the `project.yml`. |
### project push {id="project-push",tag="command"}
Upload all available files or directories listed as in the `outputs` section of
commands to a remote storage. Outputs are archived and compressed prior to
upload, and addressed in the remote storage using the output's relative path
(URL encoded), a hash of its command string and dependencies, and a hash of its
file contents. This means `push` should **never overwrite** a file in your
remote. If all the hashes match, the contents are the same and nothing happens.
If the contents are different, the new version of the file is uploaded. Deleting
obsolete files is left up to you.
Remotes can be defined in the `remotes` section of the
[`project.yml`](/usage/projects#project-yml). Under the hood, spaCy uses
[`Pathy`](https://github.com/justindujardin/pathy) to communicate with the
remote storages, so you can use any protocol that `Pathy` supports, including
[S3](https://aws.amazon.com/s3/),
[Google Cloud Storage](https://cloud.google.com/storage), and the local
filesystem, although you may need to install extra dependencies to use certain
protocols.
```bash
$ python -m spacy project push [remote] [project_dir]
```
> #### Example
>
> ```bash
> $ python -m spacy project push my_bucket
> ```
>
> ```yaml
> ### project.yml
> remotes:
> my_bucket: 's3://my-spacy-bucket'
> ```
| Name | Description |
| -------------- | --------------------------------------------------------------------------------------- |
| `remote` | The name of the remote to upload to. Defaults to `"default"`. ~~str (positional)~~ |
| `project_dir` | Path to project directory. Defaults to current working directory. ~~Path (positional)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **UPLOADS** | All project outputs that exist and are not already stored in the remote. |
### project pull {id="project-pull",tag="command"}
Download all files or directories listed as `outputs` for commands, unless they
are not already present locally. When searching for files in the remote, `pull`
won't just look at the output path, but will also consider the **command
string** and the **hashes of the dependencies**. For instance, let's say you've
previously pushed a checkpoint to the remote, but now you've changed some
hyper-parameters. Because you've changed the inputs to the command, if you run
`pull`, you won't retrieve the stale result. If you train your pipeline and push
the outputs to the remote, the outputs will be saved alongside the prior
outputs, so if you change the config back, you'll be able to fetch back the
result.
Remotes can be defined in the `remotes` section of the
[`project.yml`](/usage/projects#project-yml). Under the hood, spaCy uses
[`Pathy`](https://github.com/justindujardin/pathy) to communicate with the
remote storages, so you can use any protocol that `Pathy` supports, including
[S3](https://aws.amazon.com/s3/),
[Google Cloud Storage](https://cloud.google.com/storage), and the local
filesystem, although you may need to install extra dependencies to use certain
protocols.
```bash
$ python -m spacy project pull [remote] [project_dir]
```
> #### Example
>
> ```bash
> $ python -m spacy project pull my_bucket
> ```
>
> ```yaml
> ### project.yml
> remotes:
> my_bucket: 's3://my-spacy-bucket'
> ```
| Name | Description |
| -------------- | --------------------------------------------------------------------------------------- |
| `remote` | The name of the remote to download from. Defaults to `"default"`. ~~str (positional)~~ |
| `project_dir` | Path to project directory. Defaults to current working directory. ~~Path (positional)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **DOWNLOADS** | All project outputs that do not exist locally and can be found in the remote. |
### project document {id="project-document",tag="command"}
Auto-generate a pretty Markdown-formatted `README` for your project, based on
its [`project.yml`](/usage/projects#project-yml). Will create sections that
document the available commands, workflows and assets. The auto-generated
content will be placed between two hidden markers, so you can add your own
custom content before or after the auto-generated documentation. When you re-run
the `project document` command, only the auto-generated part is replaced.
```bash
$ python -m spacy project document [project_dir] [--output] [--no-emoji]
```
> #### Example
>
> ```bash
> $ python -m spacy project document --output README.md
> ```
<Accordion title="Example output" spaced>
For more examples, see the templates in our
[`projects`](https://github.com/explosion/projects) repo.
![Screenshot of auto-generated Markdown Readme](/images/project_document.jpg)
</Accordion>
| Name | Description |
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `project_dir` | Path to project directory. Defaults to current working directory. ~~Path (positional)~~ |
| `--output`, `-o` | Path to output file or `-` for stdout (default). If a file is specified and it already exists and contains auto-generated docs, only the auto-generated docs section is replaced. ~~Path (positional)~~ |
| `--no-emoji`, `-NE` | Don't use emoji in the titles. ~~bool (flag)~~ |
| **CREATES** | The Markdown-formatted project documentation. |
### project dvc {id="project-dvc",tag="command"}
Auto-generate [Data Version Control](https://dvc.org) (DVC) config file. Calls
[`dvc run`](https://dvc.org/doc/command-reference/run) with `--no-exec` under
the hood to generate the `dvc.yaml`. A DVC project can only define one pipeline,
so you need to specify one workflow defined in the
[`project.yml`](/usage/projects#project-yml). If no workflow is specified, the
first defined workflow is used. The DVC config will only be updated if the
`project.yml` changed. For details, see the
[DVC integration](/usage/projects#dvc) docs.
<Infobox variant="warning">
This command requires DVC to be installed and initialized in the project
directory, e.g. via [`dvc init`](https://dvc.org/doc/command-reference/init).
You'll also need to add the assets you want to track with
[`dvc add`](https://dvc.org/doc/command-reference/add).
</Infobox>
```bash
$ python -m spacy project dvc [project_dir] [workflow] [--force] [--verbose] [--quiet]
```
> #### Example
>
> ```bash
> $ git init
> $ dvc init
> $ python -m spacy project dvc all
> ```
| Name | Description |
| ----------------- | ------------------------------------------------------------------------------------------------------------- |
| `project_dir` | Path to project directory. Defaults to current working directory. ~~Path (positional)~~ |
| `workflow` | Name of workflow defined in `project.yml`. Defaults to first workflow if not set. ~~Optional[str] \(option)~~ |
| `--force`, `-F` | Force-updating config file. ~~bool (flag)~~ |
| `--verbose`, `-V` | Print more output generated by DVC. ~~bool (flag)~~ |
| `--quiet`, `-q` | Print no output generated by DVC. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | A `dvc.yaml` file in the project directory, based on the steps defined in the given workflow. |
## huggingface-hub {id="huggingface-hub",version="3.1"}
The `spacy huggingface-cli` CLI includes commands for uploading your trained
spaCy pipelines to the [Hugging Face Hub](https://huggingface.co/).
> #### Installation
>
> ```bash
> $ pip install spacy-huggingface-hub
> $ huggingface-cli login
> ```
<Infobox variant="warning">
To use this command, you need the
[`spacy-huggingface-hub`](https://github.com/explosion/spacy-huggingface-hub)
package installed. Installing the package will automatically add the
`huggingface-hub` command to the spaCy CLI.
</Infobox>
### huggingface-hub push {id="huggingface-hub-push",tag="command"}
Push a spaCy pipeline to the Hugging Face Hub. Expects a `.whl` file packaged
with [`spacy package`](/api/cli#package) and `--build wheel`. For more details,
see the spaCy project [integration](/usage/projects#huggingface_hub).
```bash
$ python -m spacy huggingface-hub push [whl_path] [--org] [--msg] [--local-repo] [--verbose]
```
> #### Example
>
> ```bash
> $ python -m spacy huggingface-hub push en_ner_fashion-0.0.0-py3-none-any.whl
> ```
| Name | Description |
| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
| `whl_path` | The path to the `.whl` file packaged with [`spacy package`](https://spacy.io/api/cli#package). ~~Path(positional)~~ |
| `--org`, `-o` | Optional name of organization to which the pipeline should be uploaded. ~~str (option)~~ |
| `--msg`, `-m` | Commit message to use for update. Defaults to `"Update spaCy pipeline"`. ~~str (option)~~ |
| `--local-repo`, `-l` | Local path to the model repository (will be created if it doesn't exist). Defaults to `hub` in the current working directory. ~~Path (option)~~ |
| `--verbose`, `-V` | Output additional info for debugging, e.g. the full generated hub metadata. ~~bool (flag)~~ |
| **UPLOADS** | The pipeline to the hub. |
Reference in New Issue View Git Blame Copy Permalink