from typing import Optional import logging from pathlib import Path from wasabi import msg import typer import srsly from .. import util from ..training.initialize import init_nlp, convert_vectors from ..language import Language from ._util import init_cli, Arg, Opt, parse_config_overrides, show_validation_error from ._util import import_code, setup_gpu @init_cli.command("vectors") def init_vectors_cli( # fmt: off lang: str = Arg(..., help="The language of the nlp object to create"), vectors_loc: Path = Arg(..., help="Vectors file in Word2Vec format", exists=True), output_dir: Path = Arg(..., help="Pipeline output directory"), prune: int = Opt(-1, "--prune", "-p", help="Optional number of vectors to prune to"), truncate: int = Opt(0, "--truncate", "-t", help="Optional number of vectors to truncate to when reading in vectors file"), name: Optional[str] = Opt(None, "--name", "-n", help="Optional name for the word vectors, e.g. en_core_web_lg.vectors"), verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"), jsonl_loc: Optional[Path] = Opt(None, "--lexemes-jsonl", "-j", help="Location of JSONL-formatted attributes file", hidden=True), # fmt: on ): """Convert word vectors for use with spaCy. Will export an nlp object that you can use in the [initialize] block of your config to initialize a model with vectors. """ util.logger.setLevel(logging.DEBUG if verbose else logging.INFO) msg.info(f"Creating blank nlp object for language '{lang}'") nlp = util.get_lang_class(lang)() if jsonl_loc is not None: update_lexemes(nlp, jsonl_loc) convert_vectors(nlp, vectors_loc, truncate=truncate, prune=prune, name=name) msg.good(f"Successfully converted {len(nlp.vocab.vectors)} vectors") nlp.to_disk(output_dir) msg.good( "Saved nlp object with vectors to output directory. You can now use the " "path to it in your config as the 'vectors' setting in [initialize].", output_dir.resolve(), ) def update_lexemes(nlp: Language, jsonl_loc: Path) -> None: # Mostly used for backwards-compatibility and may be removed in the future lex_attrs = srsly.read_jsonl(jsonl_loc) for attrs in lex_attrs: if "settings" in attrs: continue lexeme = nlp.vocab[attrs["orth"]] lexeme.set_attrs(**attrs) @init_cli.command( "nlp", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}, hidden=True, ) def init_pipeline_cli( # fmt: off ctx: typer.Context, # This is only used to read additional arguments config_path: Path = Arg(..., help="Path to config file", exists=True, allow_dash=True), output_path: Path = Arg(..., help="Output directory for the prepared data"), code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"), verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"), use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU") # fmt: on ): util.logger.setLevel(logging.DEBUG if verbose else logging.INFO) overrides = parse_config_overrides(ctx.args) import_code(code_path) setup_gpu(use_gpu) with show_validation_error(config_path): config = util.load_config(config_path, overrides=overrides) with show_validation_error(hint_fill=False): nlp = init_nlp(config, use_gpu=use_gpu) nlp.to_disk(output_path) msg.good(f"Saved initialized pipeline to {output_path}") @init_cli.command( "labels", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}, ) def init_labels_cli( # fmt: off ctx: typer.Context, # This is only used to read additional arguments config_path: Path = Arg(..., help="Path to config file", exists=True, allow_dash=True), output_path: Path = Arg(..., help="Output directory for the labels"), code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"), verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"), use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU") # fmt: on ): """Generate JSON files for the labels in the data. This helps speed up the training process, since spaCy won't have to preprocess the data to extract the labels.""" util.logger.setLevel(logging.DEBUG if verbose else logging.INFO) if not output_path.exists(): output_path.mkdir(parents=True) overrides = parse_config_overrides(ctx.args) import_code(code_path) setup_gpu(use_gpu) with show_validation_error(config_path): config = util.load_config(config_path, overrides=overrides) with show_validation_error(hint_fill=False): nlp = init_nlp(config, use_gpu=use_gpu) for name, component in nlp.pipeline: if getattr(component, "label_data", None) is not None: output_file = output_path / f"{name}.json" srsly.write_json(output_file, component.label_data) msg.good(f"Saving label data for component '{name}' to {output_file}") else: msg.info(f"No label data found for component '{name}'")