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Update pretrain docs and add unsupported loss_func error (#3860)

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* Add error to `get_vectors_loss` for unsupported loss function of `pretrain`

* Add missing "--loss-func" argument to pretrain docs. Update pretrain plac annotations to match docs.

* Add missing quotation marks
This commit is contained in:
Björn Böing 2019-06-20 10:30:44 +02:00 committed by Ines Montani
parent 4866a7ee9e
commit ebf5a04d6c
3 changed files with 16 additions and 11 deletions
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19
spacy/cli/pretrain.py
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@ -23,18 +23,19 @@ from .train import _load_pretrained_tok2vec
@plac.annotations( @plac.annotations(
texts_loc=("Path to jsonl file with texts to learn from", "positional", None, str), texts_loc=("Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the "
vectors_model=("Name or path to vectors model to learn from"), "key 'tokens'", "positional", None, str),
output_dir=("Directory to write models each epoch", "positional", None, str), vectors_model=("Name or path to spaCy model with vectors to learn from"),
output_dir=("Directory to write models to on each epoch", "positional", None, str),
width=("Width of CNN layers", "option", "cw", int), width=("Width of CNN layers", "option", "cw", int),
depth=("Depth of CNN layers", "option", "cd", int), depth=("Depth of CNN layers", "option", "cd", int),
embed_rows=("Embedding rows", "option", "er", int), embed_rows=("Number of embedding rows", "option", "er", int),
loss_func=("Loss to use for the objective. L2 or cosine", "option", "L", str), loss_func=("Loss function to use for the objective. Either 'L2' or 'cosine'", "option", "L", str),
use_vectors=("Whether to use the static vectors as input features", "flag", "uv"), use_vectors=("Whether to use the static vectors as input features", "flag", "uv"),
dropout=("Dropout", "option", "d", float), dropout=("Dropout rate", "option", "d", float),
batch_size=("Number of words per training batch", "option", "bs", int), batch_size=("Number of words per training batch", "option", "bs", int),
max_length=("Max words per example.", "option", "xw", int), max_length=("Max words per example. Longer examples are discarded", "option", "xw", int),
min_length=("Min words per example.", "option", "nw", int), min_length=("Min words per example. Shorter examples are discarded", "option", "nw", int),
seed=("Seed for random number generators", "option", "s", int), seed=("Seed for random number generators", "option", "s", int),
n_iter=("Number of iterations to pretrain", "option", "i", int), n_iter=("Number of iterations to pretrain", "option", "i", int),
n_save_every=("Save model every X batches.", "option", "se", int), n_save_every=("Save model every X batches.", "option", "se", int),
@ -250,6 +251,8 @@ def get_vectors_loss(ops, docs, prediction, objective="L2"):
loss = (d_target ** 2).sum() loss = (d_target ** 2).sum()
elif objective == "cosine": elif objective == "cosine":
loss, d_target = get_cossim_loss(prediction, target) loss, d_target = get_cossim_loss(prediction, target)
else:
raise ValueError(Errors.E139.format(loss_func=objective))
return loss, d_target return loss, d_target

1
spacy/errors.py
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@ -399,6 +399,7 @@ class Errors(object):
E138 = ("Invalid JSONL format for raw text '{text}'. Make sure the input includes either the " E138 = ("Invalid JSONL format for raw text '{text}'. Make sure the input includes either the "
"`text` or `tokens` key. For more info, see the docs:\n" "`text` or `tokens` key. For more info, see the docs:\n"
"https://spacy.io/api/cli#pretrain-jsonl") "https://spacy.io/api/cli#pretrain-jsonl")
E139 = ("Unsupported loss_function '{loss_func}'. Use either 'L2' or 'cosine'")
@add_codes @add_codes

7
website/docs/api/cli.md
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@ -285,18 +285,19 @@ improvement.
```bash ```bash
$ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir] [--width] $ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir] [--width]
[--depth] [--embed-rows] [--dropout] [--seed] [--n-iter] [--use-vectors] [--depth] [--embed-rows] [--loss_func] [--dropout] [--seed] [--n-iter] [--use-vectors]
[--n-save_every] [--n-save_every]
``` ```
| Argument | Type | Description | | Argument | Type | Description |
| ----------------------- | ---------- | --------------------------------------------------------------------------------------------------------------------------------- | | ----------------------- | ---------- | --------------------------------------------------------------------------------------------------------------------------------- |
| `texts_loc` | positional | Path to JSONL file with raw texts to learn from, with text provided as the key `"text"` or tokens as the key `tokens`. [See here](#pretrain-jsonl) for details. | | `texts_loc` | positional | Path to JSONL file with raw texts to learn from, with text provided as the key `"text"` or tokens as the key `"tokens"`. [See here](#pretrain-jsonl) for details. |
| `vectors_model` | positional | Name or path to spaCy model with vectors to learn from. | | `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. | | `output_dir` | positional | Directory to write models to on each epoch. |
| `--width`, `-cw` | option | Width of CNN layers. | | `--width`, `-cw` | option | Width of CNN layers. |
| `--depth`, `-cd` | option | Depth of CNN layers. | | `--depth`, `-cd` | option | Depth of CNN layers. |
| `--embed-rows`, `-er` | option | Number of embedding rows. | | `--embed-rows`, `-er` | option | Number of embedding rows. |
| `--loss-func`, `-L` | option | Loss function to use for the objective. Either `"L2"` or `"cosine"`. |
| `--dropout`, `-d` | option | Dropout rate. | | `--dropout`, `-d` | option | Dropout rate. |
| `--batch-size`, `-bs` | option | Number of words per training batch. | | `--batch-size`, `-bs` | option | Number of words per training batch. |
| `--max-length`, `-xw` | option | Maximum words per example. Longer examples are discarded. | | `--max-length`, `-xw` | option | Maximum words per example. Longer examples are discarded. |
@ -304,7 +305,7 @@ $ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir] [--width]
| `--seed`, `-s` | option | Seed for random number generators. | | `--seed`, `-s` | option | Seed for random number generators. |
| `--n-iter`, `-i` | option | Number of iterations to pretrain. | | `--n-iter`, `-i` | option | Number of iterations to pretrain. |
| `--use-vectors`, `-uv` | flag | Whether to use the static vectors as input features. | | `--use-vectors`, `-uv` | flag | Whether to use the static vectors as input features. |
| `--n-save_every`, `-se` | option | Save model every X batches. | | `--n-save-every`, `-se` | option | Save model every X batches. |
| `--init-tok2vec`, `-t2v` <Tag variant="new">2.1</Tag> | option | Path to pretrained weights for the token-to-vector parts of the models. See `spacy pretrain`. Experimental.| | `--init-tok2vec`, `-t2v` <Tag variant="new">2.1</Tag> | option | Path to pretrained weights for the token-to-vector parts of the models. See `spacy pretrain`. Experimental.|
| **CREATES** | weights | The pre-trained weights that can be used to initialize `spacy train`. | | **CREATES** | weights | The pre-trained weights that can be used to initialize `spacy train`. |