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