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Merge pull request #5518 from svlandeg/fix/pretrain-docs
Pretrain fixes
This commit is contained in:
Matthew Honnibal
GitHub
commit
2a8137aba9
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@ -187,7 +187,7 @@ def evaluate_textcat(tokenizer, textcat, texts, cats):
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width=("Width of CNN layers", "positional", None, int),
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embed_size=("Embedding rows", "positional", None, int),
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pretrain_iters=("Number of iterations to pretrain", "option", "pn", int),
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train_iters=("Number of iterations to pretrain", "option", "tn", int),
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train_iters=("Number of iterations to train", "option", "tn", int),
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train_examples=("Number of labelled examples", "option", "eg", int),
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vectors_model=("Name or path to vectors model to learn from"),
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)
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@ -15,6 +15,7 @@ import random
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from .._ml import create_default_optimizer
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from ..util import use_gpu as set_gpu
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from ..errors import Errors
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from ..gold import GoldCorpus
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from ..compat import path2str
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from ..lookups import Lookups
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@ -182,6 +183,7 @@ def train(
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msg.warn("Unable to activate GPU: {}".format(use_gpu))
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msg.text("Using CPU only")
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use_gpu = -1
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base_components = []
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if base_model:
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msg.text("Starting with base model '{}'".format(base_model))
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nlp = util.load_model(base_model)
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@ -227,6 +229,7 @@ def train(
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exits=1,
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)
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msg.text("Extending component from base model '{}'".format(pipe))
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base_components.append(pipe)
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disabled_pipes = nlp.disable_pipes(
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[p for p in nlp.pipe_names if p not in pipeline]
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)
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@ -299,7 +302,7 @@ def train(
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# Load in pretrained weights
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if init_tok2vec is not None:
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components = _load_pretrained_tok2vec(nlp, init_tok2vec)
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components = _load_pretrained_tok2vec(nlp, init_tok2vec, base_components)
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msg.text("Loaded pretrained tok2vec for: {}".format(components))
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# Verify textcat config
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@ -642,7 +645,7 @@ def _load_vectors(nlp, vectors):
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util.load_model(vectors, vocab=nlp.vocab)
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def _load_pretrained_tok2vec(nlp, loc):
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def _load_pretrained_tok2vec(nlp, loc, base_components):
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"""Load pretrained weights for the 'token-to-vector' part of the component
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models, which is typically a CNN. See 'spacy pretrain'. Experimental.
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"""
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@ -651,6 +654,8 @@ def _load_pretrained_tok2vec(nlp, loc):
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loaded = []
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for name, component in nlp.pipeline:
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if hasattr(component, "model") and hasattr(component.model, "tok2vec"):
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if name in base_components:
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raise ValueError(Errors.E200.format(component=name))
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component.tok2vec.from_bytes(weights_data)
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loaded.append(name)
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return loaded
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@ -568,6 +568,8 @@ class Errors(object):
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E198 = ("Unable to return {n} most similar vectors for the current vectors "
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"table, which contains {n_rows} vectors.")
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E199 = ("Unable to merge 0-length span at doc[{start}:{end}].")
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E200 = ("Specifying a base model with a pretrained component '{component}' "
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"can not be combined with adding a pretrained Tok2Vec layer.")
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@add_codes
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@ -455,7 +455,7 @@ improvement.
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```bash
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$ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir]
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[--width] [--depth] [--cnn-window] [--cnn-pieces] [--use-chars] [--sa-depth]
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[--width] [--conv-depth] [--cnn-window] [--cnn-pieces] [--use-chars] [--sa-depth]
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[--embed-rows] [--loss_func] [--dropout] [--batch-size] [--max-length]
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[--min-length] [--seed] [--n-iter] [--use-vectors] [--n-save-every]
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[--init-tok2vec] [--epoch-start]
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@ -467,7 +467,7 @@ $ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir]
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| `vectors_model` | positional | Name or path to spaCy model with vectors to learn from. |
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| `output_dir` | positional | Directory to write models to on each epoch. |
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| `--width`, `-cw` | option | Width of CNN layers. |
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| `--depth`, `-cd` | option | Depth of CNN layers. |
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| `--conv-depth`, `-cd` | option | Depth of CNN layers. |
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| `--cnn-window`, `-cW` <Tag variant="new">2.2.2</Tag> | option | Window size for CNN layers. |
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| `--cnn-pieces`, `-cP` <Tag variant="new">2.2.2</Tag> | option | Maxout size for CNN layers. `1` for [Mish](https://github.com/digantamisra98/Mish). |
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| `--use-chars`, `-chr` <Tag variant="new">2.2.2</Tag> | flag | Whether to use character-based embedding. |
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@ -541,16 +541,16 @@ $ python -m spacy init-model [lang] [output_dir] [--jsonl-loc] [--vectors-loc]
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[--prune-vectors]
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```
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| Argument | Type | Description |
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| ----------------------- | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `lang` | positional | Model language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes), e.g. `en`. |
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| `output_dir` | positional | Model output directory. Will be created if it doesn't exist. |
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| `--jsonl-loc`, `-j` | option | Optional location of JSONL-formatted [vocabulary file](/api/annotation#vocab-jsonl) with lexical attributes. |
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| `--vectors-loc`, `-v` | option | Optional location of vectors. Should be a file where the first row contains the dimensions of the vectors, followed by a space-separated Word2Vec table. File can be provided in `.txt` format or as a zipped text file in `.zip` or `.tar.gz` format. |
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| `--truncate-vectors`, `-t` | option | Number of vectors to truncate to when reading in vectors file. Defaults to `0` for no truncation. |
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| `--prune-vectors`, `-V` | option | Number of vectors to prune the vocabulary to. Defaults to `-1` for no pruning. |
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| `--vectors-name`, `-vn` | option | Name to assign to the word vectors in the `meta.json`, e.g. `en_core_web_md.vectors`. |
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| **CREATES** | model | A spaCy model containing the vocab and vectors. |
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| Argument | Type | Description |
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| -------------------------- | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `lang` | positional | Model language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes), e.g. `en`. |
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| `output_dir` | positional | Model output directory. Will be created if it doesn't exist. |
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| `--jsonl-loc`, `-j` | option | Optional location of JSONL-formatted [vocabulary file](/api/annotation#vocab-jsonl) with lexical attributes. |
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| `--vectors-loc`, `-v` | option | Optional location of vectors. Should be a file where the first row contains the dimensions of the vectors, followed by a space-separated Word2Vec table. File can be provided in `.txt` format or as a zipped text file in `.zip` or `.tar.gz` format. |
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| `--truncate-vectors`, `-t` | option | Number of vectors to truncate to when reading in vectors file. Defaults to `0` for no truncation. |
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| `--prune-vectors`, `-V` | option | Number of vectors to prune the vocabulary to. Defaults to `-1` for no pruning. |
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| `--vectors-name`, `-vn` | option | Name to assign to the word vectors in the `meta.json`, e.g. `en_core_web_md.vectors`. |
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| **CREATES** | model | A spaCy model containing the vocab and vectors. |
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## Evaluate {#evaluate new="2"}
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