Load vectors in 'spacy train'
@@ -80,16 +80,20 @@ def train(
msg.info("Using CPU")
msg.info(f"Loading config and nlp from: {config_path}")
config = Config().from_disk(config_path)
+ if config.get("training", {}).get("seed") is not None:
+ fix_random_seed(config["training"]["seed"])
with show_validation_error():
nlp, config = util.load_model_from_config(config, overrides=config_overrides)
if config["training"]["base_model"]:
- base_nlp = util.load_model(config["training"]["base_model"])
# TODO: do something to check base_nlp against regular nlp described in config?
- nlp = base_nlp
+ # If everything matches it will look something like:
+ # base_nlp = util.load_model(config["training"]["base_model"])
+ # nlp = base_nlp
+ raise NotImplementedError("base_model not supported yet.")
+ if config["training"]["vectors"] is not None:
+ util.load_vectors_into_model(nlp, config["training"]["vectors"])
verify_config(nlp)
raw_text, tag_map, morph_rules, weights_data = load_from_paths(config)
- if config["training"]["seed"] is not None:
- fix_random_seed(config["training"]["seed"])
if config["training"]["use_pytorch_for_gpu_memory"]:
# It feels kind of weird to not have a default for this.
use_pytorch_for_gpu_memory()
