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website/docs/api/cli.md
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@ -12,6 +12,7 @@ menu:
- ['train', 'train'] - ['train', 'train']
- ['pretrain', 'pretrain'] - ['pretrain', 'pretrain']
- ['evaluate', 'evaluate'] - ['evaluate', 'evaluate']
- ['apply', 'apply']
- ['find-threshold', 'find-threshold'] - ['find-threshold', 'find-threshold']
- ['assemble', 'assemble'] - ['assemble', 'assemble']
- ['package', 'package'] - ['package', 'package']
@ -1162,6 +1163,36 @@ $ python -m spacy evaluate [model] [data_path] [--output] [--code] [--gold-prepr
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | | `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | Training results and optional metrics and visualizations. | | **CREATES** | Training results and optional metrics and visualizations. |
## apply {#apply new="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 evaluation data in spaCy's [binary format](/api/data-formats#training). ~~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. ~~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`, `-g` | Batch size to use for prediction. Defaults to `1`. ~~int (option)~~ |
| `--n-process`, `-g` | 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 {#find-threshold new="3.5" tag="command"} ## find-threshold {#find-threshold new="3.5" tag="command"}
Runs prediction trials for a trained model with varying tresholds to maximize Runs prediction trials for a trained model with varying tresholds to maximize