mirror of
https://github.com/explosion/spaCy.git
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Merge remote-tracking branch 'upstream/master' into chore/update-develop-from-master-v3.2-3
This commit is contained in:
Adriane Boyd
commit
2d430958e1
1
.github/azure-steps.yml
vendored
1
.github/azure-steps.yml
vendored
|
|
@ -27,6 +27,7 @@ steps:
|
|||
|
||||
- script: python -m mypy spacy
|
||||
displayName: 'Run mypy'
|
||||
condition: ne(variables['python_version'], '3.10')
|
||||
|
||||
- task: DeleteFiles@1
|
||||
inputs:
|
||||
|
|
|
|||
|
|
@ -42,7 +42,7 @@ jobs:
|
|||
imageName: "ubuntu-18.04"
|
||||
python.version: "3.6"
|
||||
# Python36Windows:
|
||||
# imageName: "vs2017-win2016"
|
||||
# imageName: "windows-2019"
|
||||
# python.version: "3.6"
|
||||
# Python36Mac:
|
||||
# imageName: "macos-10.14"
|
||||
|
|
@ -51,7 +51,7 @@ jobs:
|
|||
# imageName: "ubuntu-18.04"
|
||||
# python.version: "3.7"
|
||||
Python37Windows:
|
||||
imageName: "vs2017-win2016"
|
||||
imageName: "windows-2019"
|
||||
python.version: "3.7"
|
||||
# Python37Mac:
|
||||
# imageName: "macos-10.14"
|
||||
|
|
@ -60,7 +60,7 @@ jobs:
|
|||
# imageName: "ubuntu-18.04"
|
||||
# python.version: "3.8"
|
||||
# Python38Windows:
|
||||
# imageName: "vs2017-win2016"
|
||||
# imageName: "windows-2019"
|
||||
# python.version: "3.8"
|
||||
Python38Mac:
|
||||
imageName: "macos-10.14"
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||||
|
|
@ -68,12 +68,21 @@ jobs:
|
|||
Python39Linux:
|
||||
imageName: "ubuntu-18.04"
|
||||
python.version: "3.9"
|
||||
Python39Windows:
|
||||
imageName: "vs2017-win2016"
|
||||
python.version: "3.9"
|
||||
Python39Mac:
|
||||
imageName: "macos-10.14"
|
||||
python.version: "3.9"
|
||||
# Python39Windows:
|
||||
# imageName: "windows-2019"
|
||||
# python.version: "3.9"
|
||||
# Python39Mac:
|
||||
# imageName: "macos-10.14"
|
||||
# python.version: "3.9"
|
||||
Python310Linux:
|
||||
imageName: "ubuntu-20.04"
|
||||
python.version: "3.10"
|
||||
Python310Windows:
|
||||
imageName: "windows-2019"
|
||||
python.version: "3.10"
|
||||
Python310Mac:
|
||||
imageName: "macos-10.15"
|
||||
python.version: "3.10"
|
||||
maxParallel: 4
|
||||
pool:
|
||||
vmImage: $(imageName)
|
||||
|
|
|
|||
|
|
@ -2,4 +2,5 @@
|
|||
numpy==1.15.0; python_version<='3.7'
|
||||
numpy==1.17.3; python_version=='3.8'
|
||||
numpy==1.19.3; python_version=='3.9'
|
||||
numpy; python_version>='3.10'
|
||||
numpy==1.21.3; python_version=='3.10'
|
||||
numpy; python_version>='3.11'
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ requires = [
|
|||
"cymem>=2.0.2,<2.1.0",
|
||||
"preshed>=3.0.2,<3.1.0",
|
||||
"murmurhash>=0.28.0,<1.1.0",
|
||||
"thinc>=8.0.11,<8.1.0",
|
||||
"thinc>=8.0.12,<8.1.0",
|
||||
"blis>=0.4.0,<0.8.0",
|
||||
"pathy",
|
||||
"numpy>=1.15.0",
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ spacy-legacy>=3.0.8,<3.1.0
|
|||
spacy-loggers>=1.0.0,<2.0.0
|
||||
cymem>=2.0.2,<2.1.0
|
||||
preshed>=3.0.2,<3.1.0
|
||||
thinc>=8.0.11,<8.1.0
|
||||
thinc>=8.0.12,<8.1.0
|
||||
blis>=0.4.0,<0.8.0
|
||||
ml_datasets>=0.2.0,<0.3.0
|
||||
murmurhash>=0.28.0,<1.1.0
|
||||
|
|
|
|||
11
setup.cfg
11
setup.cfg
|
|
@ -21,6 +21,7 @@ classifiers =
|
|||
Programming Language :: Python :: 3.7
|
||||
Programming Language :: Python :: 3.8
|
||||
Programming Language :: Python :: 3.9
|
||||
Programming Language :: Python :: 3.10
|
||||
Topic :: Scientific/Engineering
|
||||
project_urls =
|
||||
Release notes = https://github.com/explosion/spaCy/releases
|
||||
|
|
@ -37,7 +38,7 @@ setup_requires =
|
|||
cymem>=2.0.2,<2.1.0
|
||||
preshed>=3.0.2,<3.1.0
|
||||
murmurhash>=0.28.0,<1.1.0
|
||||
thinc>=8.0.11,<8.1.0
|
||||
thinc>=8.0.12,<8.1.0
|
||||
install_requires =
|
||||
# Our libraries
|
||||
spacy-legacy>=3.0.8,<3.1.0
|
||||
|
|
@ -45,7 +46,7 @@ install_requires =
|
|||
murmurhash>=0.28.0,<1.1.0
|
||||
cymem>=2.0.2,<2.1.0
|
||||
preshed>=3.0.2,<3.1.0
|
||||
thinc>=8.0.11,<8.1.0
|
||||
thinc>=8.0.12,<8.1.0
|
||||
blis>=0.4.0,<0.8.0
|
||||
wasabi>=0.8.1,<1.1.0
|
||||
srsly>=2.4.1,<3.0.0
|
||||
|
|
@ -97,6 +98,12 @@ cuda111 =
|
|||
cupy-cuda111>=5.0.0b4,<10.0.0
|
||||
cuda112 =
|
||||
cupy-cuda112>=5.0.0b4,<10.0.0
|
||||
cuda113 =
|
||||
cupy-cuda113>=5.0.0b4,<10.0.0
|
||||
cuda114 =
|
||||
cupy-cuda114>=5.0.0b4,<10.0.0
|
||||
apple =
|
||||
thinc-apple-ops>=0.0.4,<1.0.0
|
||||
# Language tokenizers with external dependencies
|
||||
ja =
|
||||
sudachipy>=0.4.9
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from typing import Optional, Dict, Any
|
||||
from typing import Optional, Dict, Any, Union
|
||||
from pathlib import Path
|
||||
from wasabi import msg
|
||||
import typer
|
||||
|
|
@ -46,12 +46,14 @@ def train_cli(
|
|||
|
||||
|
||||
def train(
|
||||
config_path: Path,
|
||||
output_path: Optional[Path] = None,
|
||||
config_path: Union[str, Path],
|
||||
output_path: Optional[Union[str, Path]] = None,
|
||||
*,
|
||||
use_gpu: int = -1,
|
||||
overrides: Dict[str, Any] = util.SimpleFrozenDict(),
|
||||
):
|
||||
config_path = util.ensure_path(config_path)
|
||||
output_path = util.ensure_path(output_path)
|
||||
# Make sure all files and paths exists if they are needed
|
||||
if not config_path or (str(config_path) != "-" and not config_path.exists()):
|
||||
msg.fail("Config file not found", config_path, exits=1)
|
||||
|
|
|
|||
|
|
@ -893,6 +893,7 @@ class Errors:
|
|||
"filename. Specify an epoch to resume from.")
|
||||
E1021 = ("`pos` value \"{pp}\" is not a valid Universal Dependencies tag. "
|
||||
"Non-UD tags should use the `tag` property.")
|
||||
E1022 = ("Words must be of type str or int, but input is of type '{wtype}'")
|
||||
|
||||
|
||||
# Deprecated model shortcuts, only used in errors and warnings
|
||||
|
|
|
|||
|
|
@ -25,6 +25,7 @@ def test_build_dependencies():
|
|||
"sudachipy",
|
||||
"sudachidict_core",
|
||||
"spacy-pkuseg",
|
||||
"thinc-apple-ops",
|
||||
]
|
||||
|
||||
# check requirements.txt
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
from typing import Dict, Iterable, Callable
|
||||
import pytest
|
||||
from thinc.api import Config
|
||||
from thinc.api import Config, fix_random_seed
|
||||
from spacy import Language
|
||||
from spacy.util import load_model_from_config, registry, resolve_dot_names
|
||||
from spacy.schemas import ConfigSchemaTraining
|
||||
|
|
@ -64,8 +64,8 @@ def test_readers():
|
|||
@pytest.mark.parametrize(
|
||||
"reader,additional_config",
|
||||
[
|
||||
("ml_datasets.imdb_sentiment.v1", {"train_limit": 10, "dev_limit": 2}),
|
||||
("ml_datasets.dbpedia.v1", {"train_limit": 10, "dev_limit": 2}),
|
||||
("ml_datasets.imdb_sentiment.v1", {"train_limit": 10, "dev_limit": 10}),
|
||||
("ml_datasets.dbpedia.v1", {"train_limit": 10, "dev_limit": 10}),
|
||||
("ml_datasets.cmu_movies.v1", {"limit": 10, "freq_cutoff": 200, "split": 0.8}),
|
||||
],
|
||||
)
|
||||
|
|
@ -93,6 +93,7 @@ def test_cat_readers(reader, additional_config):
|
|||
factory = "textcat_multilabel"
|
||||
"""
|
||||
config = Config().from_str(nlp_config_string)
|
||||
fix_random_seed(config["training"]["seed"])
|
||||
config["corpora"]["@readers"] = reader
|
||||
config["corpora"].update(additional_config)
|
||||
nlp = load_model_from_config(config, auto_fill=True)
|
||||
|
|
|
|||
|
|
@ -194,11 +194,12 @@ cdef class Doc:
|
|||
|
||||
vocab (Vocab): A vocabulary object, which must match any models you
|
||||
want to use (e.g. tokenizer, parser, entity recognizer).
|
||||
words (Optional[List[str]]): A list of unicode strings to add to the document
|
||||
as words. If `None`, defaults to empty list.
|
||||
spaces (Optional[List[bool]]): A list of boolean values, of the same length as
|
||||
words. True means that the word is followed by a space, False means
|
||||
it is not. If `None`, defaults to `[True]*len(words)`
|
||||
words (Optional[List[Union[str, int]]]): A list of unicode strings or
|
||||
hash values to add to the document as words. If `None`, defaults to
|
||||
empty list.
|
||||
spaces (Optional[List[bool]]): A list of boolean values, of the same
|
||||
length as `words`. `True` means that the word is followed by a space,
|
||||
`False` means it is not. If `None`, defaults to `[True]*len(words)`
|
||||
user_data (dict or None): Optional extra data to attach to the Doc.
|
||||
tags (Optional[List[str]]): A list of unicode strings, of the same
|
||||
length as words, to assign as token.tag. Defaults to None.
|
||||
|
|
@ -266,7 +267,10 @@ cdef class Doc:
|
|||
elif isinstance(word, bytes):
|
||||
raise ValueError(Errors.E028.format(value=word))
|
||||
else:
|
||||
lexeme = self.vocab.get_by_orth(self.mem, word)
|
||||
try:
|
||||
lexeme = self.vocab.get_by_orth(self.mem, word)
|
||||
except TypeError:
|
||||
raise TypeError(Errors.E1022.format(wtype=type(word)))
|
||||
self.push_back(lexeme, has_space)
|
||||
|
||||
if heads is not None:
|
||||
|
|
|
|||
|
|
@ -820,6 +820,29 @@ $ python -m spacy train [config_path] [--output] [--code] [--verbose] [--gpu-id]
|
|||
| 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 {#train-function new="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 {#pretrain new="2.1" tag="command,experimental"}
|
||||
|
||||
Pretrain the "token to vector" ([`Tok2vec`](/api/tok2vec)) layer of pipeline
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@ Construct a `Doc` object. The most common way to get a `Doc` object is via the
|
|||
| Name | Description |
|
||||
| ---------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `vocab` | A storage container for lexical types. ~~Vocab~~ |
|
||||
| `words` | A list of strings to add to the container. ~~Optional[List[str]]~~ |
|
||||
| `words` | A list of strings or integer hash values to add to the document as words. ~~Optional[List[Union[str,int]]]~~ |
|
||||
| `spaces` | A list of boolean values indicating whether each word has a subsequent space. Must have the same length as `words`, if specified. Defaults to a sequence of `True`. ~~Optional[List[bool]]~~ |
|
||||
| _keyword-only_ | |
|
||||
| `user\_data` | Optional extra data to attach to the Doc. ~~Dict~~ |
|
||||
|
|
|
|||
|
|
@ -773,17 +773,17 @@ from the specified model. Intended for use in `[initialize.before_init]`.
|
|||
> after_pipeline_creation = {"@callbacks":"spacy.models_with_nvtx_range.v1"}
|
||||
> ```
|
||||
|
||||
Recursively wrap the models in each pipe using [NVTX](https://nvidia.github.io/NVTX/)
|
||||
range markers. These markers aid in GPU profiling by attributing specific operations
|
||||
to a ~~Model~~'s forward or backprop passes.
|
||||
Recursively wrap the models in each pipe using
|
||||
[NVTX](https://nvidia.github.io/NVTX/) range markers. These markers aid in GPU
|
||||
profiling by attributing specific operations to a ~~Model~~'s forward or
|
||||
backprop passes.
|
||||
|
||||
| Name | Description |
|
||||
|------------------|------------------------------------------------------------------------------------------------------------------------------|
|
||||
| ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `forward_color` | Color identifier for forward passes. Defaults to `-1`. ~~int~~ |
|
||||
| `backprop_color` | Color identifier for backpropagation passes. Defaults to `-1`. ~~int~~ |
|
||||
| **CREATES** | A function that takes the current `nlp` and wraps forward/backprop passes in NVTX ranges. ~~Callable[[Language], Language]~~ |
|
||||
|
||||
|
||||
## Training data and alignment {#gold source="spacy/training"}
|
||||
|
||||
### training.offsets_to_biluo_tags {#offsets_to_biluo_tags tag="function"}
|
||||
|
|
|
|||
|
|
@ -71,13 +71,14 @@ spaCy's [`setup.cfg`](%%GITHUB_SPACY/setup.cfg) for details on what's included.
|
|||
> $ pip install %%SPACY_PKG_NAME[lookups,transformers]%%SPACY_PKG_FLAGS
|
||||
> ```
|
||||
|
||||
| Name | Description |
|
||||
| ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `lookups` | Install [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) for data tables for lemmatization and lexeme normalization. The data is serialized with trained pipelines, so you only need this package if you want to train your own models. |
|
||||
| `transformers` | Install [`spacy-transformers`](https://github.com/explosion/spacy-transformers). The package will be installed automatically when you install a transformer-based pipeline. |
|
||||
| `ray` | Install [`spacy-ray`](https://github.com/explosion/spacy-ray) to add CLI commands for [parallel training](/usage/training#parallel-training). |
|
||||
| `cuda`, ... | Install spaCy with GPU support provided by [CuPy](https://cupy.chainer.org) for your given CUDA version. See the GPU [installation instructions](#gpu) for details and options. |
|
||||
| `ja`, `ko`, `th`, `zh` | Install additional dependencies required for tokenization for the [languages](/usage/models#languages). |
|
||||
| Name | Description |
|
||||
| ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `lookups` | Install [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) for data tables for lemmatization and lexeme normalization. The data is serialized with trained pipelines, so you only need this package if you want to train your own models. |
|
||||
| `transformers` | Install [`spacy-transformers`](https://github.com/explosion/spacy-transformers). The package will be installed automatically when you install a transformer-based pipeline. |
|
||||
| `ray` | Install [`spacy-ray`](https://github.com/explosion/spacy-ray) to add CLI commands for [parallel training](/usage/training#parallel-training). |
|
||||
| `cuda`, ... | Install spaCy with GPU support provided by [CuPy](https://cupy.chainer.org) for your given CUDA version. See the GPU [installation instructions](#gpu) for details and options. |
|
||||
| `apple` | Install [`thinc-apple-ops`](https://github.com/explosion/thinc-apple-ops) to improve performance on an Apple M1. |
|
||||
| `ja`, `ko`, `th` | Install additional dependencies required for tokenization for the [languages](/usage/models#languages). |
|
||||
|
||||
### conda {#conda}
|
||||
|
||||
|
|
|
|||
|
|
@ -301,8 +301,6 @@ fly without having to save to and load from disk.
|
|||
$ python -m spacy init config - --lang en --pipeline ner,textcat --optimize accuracy | python -m spacy train - --paths.train ./corpus/train.spacy --paths.dev ./corpus/dev.spacy
|
||||
```
|
||||
|
||||
<!-- TODO: add reference to Prodigy's commands once Prodigy nightly is available -->
|
||||
|
||||
### Using variable interpolation {#config-interpolation}
|
||||
|
||||
Another very useful feature of the config system is that it supports variable
|
||||
|
|
@ -1647,7 +1645,7 @@ workers are stuck waiting for it to complete before they can continue.
|
|||
|
||||
## Internal training API {#api}
|
||||
|
||||
<Infobox variant="warning">
|
||||
<Infobox variant="danger">
|
||||
|
||||
spaCy gives you full control over the training loop. However, for most use
|
||||
cases, it's recommended to train your pipelines via the
|
||||
|
|
@ -1659,6 +1657,32 @@ typically give you everything you need to train fully custom pipelines with
|
|||
|
||||
</Infobox>
|
||||
|
||||
### Training from a Python script {#api-train new="3.2"}
|
||||
|
||||
If you want to run the training from a Python script instead of using the
|
||||
[`spacy train`](/api/cli#train) CLI command, you can call into the
|
||||
[`train`](/api/cli#train-function) helper function directly. It takes the path
|
||||
to the config file, an optional output directory and an optional dictionary of
|
||||
[config overrides](#config-overrides).
|
||||
|
||||
```python
|
||||
from spacy.cli.train import train
|
||||
|
||||
train("./config.cfg", overrides={"paths.train": "./train.spacy", "paths.dev": "./dev.spacy"})
|
||||
```
|
||||
|
||||
### Internal training loop API {#api-loop}
|
||||
|
||||
<Infobox variant="warning">
|
||||
|
||||
This section documents how the training loop and updates to the `nlp` object
|
||||
work internally. You typically shouldn't have to implement this in Python unless
|
||||
you're writing your own trainable components. To train a pipeline, use
|
||||
[`spacy train`](/api/cli#train) or the [`train`](/api/cli#train-function) helper
|
||||
function instead.
|
||||
|
||||
</Infobox>
|
||||
|
||||
The [`Example`](/api/example) object contains annotated training data, also
|
||||
called the **gold standard**. It's initialized with a [`Doc`](/api/doc) object
|
||||
that will hold the predictions, and another `Doc` object that holds the
|
||||
|
|
|
|||
|
|
@ -1138,7 +1138,7 @@
|
|||
{
|
||||
"id": "deplacy",
|
||||
"slogan": "CUI-based Tree Visualizer for Universal Dependencies and Immediate Catena Analysis",
|
||||
"discreption": "Simple dependency visualizer for [spaCy](https://spacy.io/), [UniDic2UD](https://pypi.org/project/unidic2ud), [Stanza](https://stanfordnlp.github.io/stanza/), [NLP-Cube](https://github.com/Adobe/NLP-Cube), [Trankit](https://github.com/nlp-uoregon/trankit), etc.",
|
||||
"description": "Simple dependency visualizer for [spaCy](https://spacy.io/), [UniDic2UD](https://pypi.org/project/unidic2ud), [Stanza](https://stanfordnlp.github.io/stanza/), [NLP-Cube](https://github.com/Adobe/NLP-Cube), [Trankit](https://github.com/nlp-uoregon/trankit), etc.",
|
||||
"github": "KoichiYasuoka/deplacy",
|
||||
"image": "https://i.imgur.com/6uOI4Op.png",
|
||||
"code_example": [
|
||||
|
|
@ -1270,7 +1270,7 @@
|
|||
"description": "`textacy` is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance `spacy` library. With the fundamentals – tokenization, part-of-speech tagging, dependency parsing, etc. – delegated to another library, `textacy` focuses on the tasks that come before and follow after.",
|
||||
"github": "chartbeat-labs/textacy",
|
||||
"pip": "textacy",
|
||||
"url": "https://chartbeat-labs.github.io/textacy/",
|
||||
"url": "https://github.com/chartbeat-labs/textacy",
|
||||
"author": "Burton DeWilde",
|
||||
"author_links": {
|
||||
"github": "bdewilde",
|
||||
|
|
|
|||
|
|
@ -4,10 +4,12 @@ import { StaticQuery, graphql } from 'gatsby'
|
|||
import { Quickstart, QS } from '../components/quickstart'
|
||||
import { repo, DEFAULT_BRANCH } from '../components/util'
|
||||
|
||||
const DEFAULT_OS = 'mac'
|
||||
const DEFAULT_PLATFORM = 'x86'
|
||||
const DEFAULT_MODELS = ['en']
|
||||
const DEFAULT_OPT = 'efficiency'
|
||||
const DEFAULT_HARDWARE = 'cpu'
|
||||
const DEFAULT_CUDA = 'cuda102'
|
||||
const DEFAULT_CUDA = 'cuda113'
|
||||
const CUDA = {
|
||||
'8.0': 'cuda80',
|
||||
'9.0': 'cuda90',
|
||||
|
|
@ -19,11 +21,15 @@ const CUDA = {
|
|||
'11.0': 'cuda110',
|
||||
'11.1': 'cuda111',
|
||||
'11.2': 'cuda112',
|
||||
'11.3': 'cuda113',
|
||||
'11.4': 'cuda114',
|
||||
}
|
||||
const LANG_EXTRAS = ['ja'] // only for languages with models
|
||||
|
||||
const QuickstartInstall = ({ id, title }) => {
|
||||
const [train, setTrain] = useState(false)
|
||||
const [platform, setPlatform] = useState(DEFAULT_PLATFORM)
|
||||
const [os, setOs] = useState(DEFAULT_OS)
|
||||
const [hardware, setHardware] = useState(DEFAULT_HARDWARE)
|
||||
const [cuda, setCuda] = useState(DEFAULT_CUDA)
|
||||
const [selectedModels, setModels] = useState(DEFAULT_MODELS)
|
||||
|
|
@ -33,15 +39,19 @@ const QuickstartInstall = ({ id, title }) => {
|
|||
config: v => setTrain(v.includes('train')),
|
||||
models: setModels,
|
||||
optimize: v => setEfficiency(v.includes('efficiency')),
|
||||
platform: v => setPlatform(v[0]),
|
||||
os: v => setOs(v[0]),
|
||||
}
|
||||
const showDropdown = {
|
||||
hardware: () => hardware === 'gpu',
|
||||
}
|
||||
const modelExtras = train ? selectedModels.filter(m => LANG_EXTRAS.includes(m)) : []
|
||||
const apple = os === 'mac' && platform === 'arm'
|
||||
const pipExtras = [
|
||||
hardware === 'gpu' && cuda,
|
||||
train && 'transformers',
|
||||
train && 'lookups',
|
||||
apple && 'apple',
|
||||
...modelExtras,
|
||||
]
|
||||
.filter(e => e)
|
||||
|
|
@ -62,6 +72,16 @@ const QuickstartInstall = ({ id, title }) => {
|
|||
{ id: 'windows', title: 'Windows' },
|
||||
{ id: 'linux', title: 'Linux' },
|
||||
],
|
||||
defaultValue: DEFAULT_OS,
|
||||
},
|
||||
{
|
||||
id: 'platform',
|
||||
title: 'Platform',
|
||||
options: [
|
||||
{ id: 'x86', title: 'x86', checked: true },
|
||||
{ id: 'arm', title: 'ARM / M1' },
|
||||
],
|
||||
defaultValue: DEFAULT_PLATFORM,
|
||||
},
|
||||
{
|
||||
id: 'package',
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user