如何基于已训练的Spacy Transformers模型恢复命名实体识别（NER）训练

Let's fix this issue step by step. The error you're seeing comes from misusing the vectors parameter in your config — that's meant for loading word vectors (like spaCy's pre-trained vector sets), not your trained NER model weights. Here's how to properly resume training from your model-best directory:

1. Use the --base-model Flag with spacy train (Recommended) #

The simplest way to resume training is to use spaCy's built-in --base-model argument when running the training command. This tells spaCy to load your existing trained model and continue training with new data instead of initializing from scratch.

Step-by-Step Command: #

First, make sure your new training/validation data is in spaCy's binary format (.spacy). Then run:

python -m spacy train config.cfg \
  --paths.train ./your_new_train_data.spacy \
  --paths.dev ./your_new_dev_data.spacy \
  --base-model ./model-best \
  --output ./new_model_output

What This Does: #

• Loads all pre-trained weights from ./model-best (including the transformer and NER components)
• Uses your new training data to update the model
• Saves the updated model to ./new_model_output (you can change this path as needed)

2. Adjust Config Settings (Optional but Recommended) #

You don't need to modify the [initialize] section at all, but you may want to tweak training parameters to fit your resume training scenario:

• Reduce max_steps/max_epochs: Since your model is already 90% accurate, you don't need to run 20000 steps again. Try setting max_steps = 5000 or max_epochs = 10 in the [training] section.
• Lower dropout: Dropout is used to prevent overfitting during initial training. For resume training, set dropout = 0.05 (down from 0.1) to avoid forgetting the existing good weights.
• Verify Transformer Name: Ensure [components.transformer.model.name] matches the one used in model-best (in your config it's roberta-base — this should stay the same to avoid compatibility issues).

3. Why Your Initial Approach Failed #

The error message spells it out clearly:

If your pipeline was already initialized/trained before, call 'resume_training' instead of 'initialize', or initialize only the components that are new.

You tried to set vectors = ./model-best/ner, but:

• vectors expects a path to a word vector file (e.g., en_core_web_lg/vectors), not a trained NER component directory.
• Using this parameter forces spaCy to re-initialize the pipeline, which conflicts with loading your pre-trained weights.

4. Alternative: Resume Training via Python Code #

If you prefer to use a custom training loop instead of the command line, you can load your model directly and call resume_training():

import spacy
from spacy.training import Example
from spacy.util import minibatch, compounding

# Load your pre-trained model
nlp = spacy.load("./model-best")
ner = nlp.get_pipe("ner")

# Add new entity labels if you have them (skip if no new labels)
# for new_label in ["NEW_ENTITY"]:
#     ner.add_label(new_label)

# Prepare your new training data (format: list of (text, annotations))
train_data = [
    ("Your new training text here", {"entities": [(0, 5, "ENTITY_TYPE")]}),
    # ... add more examples
]
dev_data = [
    ("Your validation text here", {"entities": [(0, 5, "ENTITY_TYPE")]}),
    # ... add more examples
]

# Convert data to spaCy Example objects
train_examples = [Example.from_dict(nlp.make_doc(text), anns) for text, anns in train_data]
dev_examples = [Example.from_dict(nlp.make_doc(text), anns) for text, anns in dev_data]

# Initialize optimizer for resume training
optimizer = nlp.resume_training()
batch_sizes = compounding(4.0, 32.0, 1.001)

# Run training loop
for epoch in range(10):
    losses = {}
    # Shuffle data each epoch to avoid bias
    spacy.util.fix_random_seed(42)
    batches = minibatch(train_examples, size=batch_sizes)
    for batch in batches:
        nlp.update(batch, sgd=optimizer, losses=losses)
    print(f"Epoch {epoch+1}: Losses = {losses}")
    
    # Evaluate on dev data to track progress
    scores = nlp.evaluate(dev_examples)
    print(f"F1 Score: {scores['ents_f']:.2f}, Precision: {scores['ents_p']:.2f}, Recall: {scores['ents_r']:.2f}")

# Save the updated model
nlp.to_disk("./resumed_model")

Final Notes #

• Always back up your original model-best directory before starting resume training, just in case something goes wrong.
• If you added new entity labels, make sure your training data includes plenty of examples for those labels so the model can learn to recognize them effectively.

内容的提问来源于stack exchange，提问作者user12188405