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Expose batch size and length caps on CLI for pretrain (#3417)

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Add and document CLI options for batch size, max doc length, min doc length for `spacy pretrain`.

Also improve CLI output.

Closes #3216 

## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
This commit is contained in:
Matthew Honnibal 2019-03-16 21:38:45 +01:00 committed by Ines Montani
parent 58d562d9b0
commit 62afa64a8d
2 changed files with 31 additions and 5 deletions
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33
spacy/cli/pretrain.py
View File

@ -29,6 +29,9 @@ from .. import util
embed_rows=("Embedding rows", "option", "er", int),
use_vectors=("Whether to use the static vectors as input features", "flag", "uv"),
dropout=("Dropout", "option", "d", float),
batch_size=("Number of words per training batch", "option", "bs", int),
max_length=("Max words per example.", "option", "xw", int),
min_length=("Min words per example.", "option", "nw", int),
seed=("Seed for random number generators", "option", "s", float),
nr_iter=("Number of iterations to pretrain", "option", "i", int),
)
@ -42,6 +45,9 @@ def pretrain(
use_vectors=False,
dropout=0.2,
nr_iter=1000,
batch_size=3000,
max_length=500,
min_length=5,
seed=0,
):
"""
@ -109,9 +115,14 @@ def pretrain(
msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
for epoch in range(nr_iter):
for batch in util.minibatch_by_words(
((text, None) for text in texts), size=3000
((text, None) for text in texts), size=batch_size
):
docs = make_docs(nlp, [text for (text, _) in batch])
docs = make_docs(
nlp,
[text for (text, _) in batch],
max_length=max_length,
min_length=min_length,
)
loss = make_update(model, docs, optimizer, drop=dropout)
progress = tracker.update(epoch, loss, docs)
if progress:
@ -152,7 +163,7 @@ def make_update(model, docs, optimizer, drop=0.0, objective="L2"):
return float(loss)
def make_docs(nlp, batch, min_length=1, max_length=500):
def make_docs(nlp, batch, min_length, max_length):
docs = []
for record in batch:
text = record["text"]
@ -241,11 +252,23 @@ class ProgressTracker(object):
status = (
epoch,
self.nr_word,
"%.8f" % self.loss,
"%.8f" % loss_per_word,
_smart_round(self.loss, width=10),
_smart_round(loss_per_word, width=6),
int(wps),
)
self.prev_loss = float(self.loss)
return status
else:
return None
def _smart_round(figure, width=10, max_decimal=4):
"""Round large numbers as integers, smaller numbers as decimals."""
n_digits = len(str(int(figure)))
n_decimal = width - (n_digits + 1)
if n_decimal <= 1:
return str(int(figure))
else:
n_decimal = min(n_decimal, max_decimal)
format_str = "%." + str(n_decimal) + "f"
return format_str % figure

3
website/docs/api/cli.md
View File

@ -296,6 +296,9 @@ $ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir] [--width]
| `--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. |