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Make flag shortcut consistent and document

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This commit is contained in:
Ines Montani 2019-04-22 14:23:44 +02:00
parent 52658c80d5
commit 7917ce2f73
2 changed files with 19 additions and 18 deletions
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2
spacy/cli/evaluate.py
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@ -17,7 +17,7 @@ from .. import displacy
gpu_id=("Use GPU", "option", "g", int),
displacy_path=("Directory to output rendered parses as HTML", "option", "dp", str),
displacy_limit=("Limit of parses to render as HTML", "option", "dl", int),
return_scores=("Return dict containing model scores", "flag", "r", bool),
return_scores=("Return dict containing model scores", "flag", "R", bool),
)
def evaluate(
model,

35
website/docs/api/cli.md
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@ -288,23 +288,23 @@ $ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir] [--width]
[--n-save_every]
```
| Argument | Type | Description |
| ---------------------- | ---------- | --------------------------------------------------------------------------------------------------------------------------------- |
| `texts_loc` | positional | Path to JSONL file with raw texts to learn from, with text provided as the key `"text"`. [See here](#pretrain-jsonl) for details. |
| `vectors_model` | positional | Name or path to spaCy model with vectors to learn from. |
| `output_dir` | positional | Directory to write models to on each epoch. |
| `--width`, `-cw` | option | Width of CNN layers. |
| `--depth`, `-cd` | option | Depth of CNN layers. |
| `--embed-rows`, `-er` | option | Number of embedding rows. |
| `--dropout`, `-d` | option | Dropout rate. |
| `--batch-size`, `-bs` | option | Number of words per training batch. |
| `--max-length`, `-xw` | option | Maximum words per example. Longer examples are discarded. |
| `--min-length`, `-nw` | option | Minimum words per example. Shorter examples are discarded. |
| `--seed`, `-s` | option | Seed for random number generators. |
| `--n-iter`, `-i` | option | Number of iterations to pretrain. |
| `--use-vectors`, `-uv` | flag | Whether to use the static vectors as input features. |
| `--n-save_every`, `-se` | option | Save model every X batches. |
| **CREATES** | weights | The pre-trained weights that can be used to initialize `spacy train`. |
| Argument | Type | Description |
| ----------------------- | ---------- | --------------------------------------------------------------------------------------------------------------------------------- |
| `texts_loc` | positional | Path to JSONL file with raw texts to learn from, with text provided as the key `"text"`. [See here](#pretrain-jsonl) for details. |
| `vectors_model` | positional | Name or path to spaCy model with vectors to learn from. |
| `output_dir` | positional | Directory to write models to on each epoch. |
| `--width`, `-cw` | option | Width of CNN layers. |
| `--depth`, `-cd` | option | Depth of CNN layers. |
| `--embed-rows`, `-er` | option | Number of embedding rows. |
| `--dropout`, `-d` | option | Dropout rate. |
| `--batch-size`, `-bs` | option | Number of words per training batch. |
| `--max-length`, `-xw` | option | Maximum words per example. Longer examples are discarded. |
| `--min-length`, `-nw` | option | Minimum words per example. Shorter examples are discarded. |
| `--seed`, `-s` | option | Seed for random number generators. |
| `--n-iter`, `-i` | option | Number of iterations to pretrain. |
| `--use-vectors`, `-uv` | flag | Whether to use the static vectors as input features. |
| `--n-save_every`, `-se` | option | Save model every X batches. |
| **CREATES** | weights | The pre-trained weights that can be used to initialize `spacy train`. |
### JSONL format for raw text {#pretrain-jsonl}
@ -388,6 +388,7 @@ $ python -m spacy evaluate [model] [data_path] [--displacy-path] [--displacy-lim
| `--displacy-limit`, `-dl` | option | 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. |
| `--gpu-id`, `-g` | option | GPU to use, if any. Defaults to `-1` for CPU. |
| `--gold-preproc`, `-G` | flag | Use gold preprocessing. |
| `--return-scores`, `-R` | flag | Return dict containing model scores. |
| **CREATES** | `stdout`, HTML | Training results and optional displaCy visualizations. |
## Package {#package}