使用spaCy预训练NER模型时出现广播输入数组形状错误求助

Hey there, let's figure out why you're hitting that shape mismatch error when using spaCy's pre-train for your NER task, and how to fix it.

What's Causing the Error? #

The core issue here is a dimension mismatch between the pre-trained token2vec weights you generated (model999.bin) and the original en_core_web_lg model's token2vec layer.

• en_core_web_lg (in spaCy 2.1.3) uses a token2vec layer with an output dimension of 480, powered by a CNN architecture with window sizes 1, 3, 5.
• When you ran spacy pre-train without specifying the --width parameter, spaCy defaults to a dimension of 384. That's why you're seeing the error: it's trying to broadcast a (96,3,384) array into a (96,3,480) shape—they don't line up.

Step-by-Step Fixes #

Let's walk through how to resolve this:

1. Re-run spacy pre-train with matching dimensions

You need to explicitly set the token2vec width and window sizes to match en_core_web_lg's architecture. Run this command instead:

spacy pre-train ./your_config.json ./your_training_data.jsonl ./output_dir \
  --base-model ./saved_en_core_web_lg \
  --width 480 \
  --window-sizes 1 3 5

• --width 480: Ensures the pre-trained token2vec outputs match the original model's dimension.
• --window-sizes 1 3 5: Matches the CNN window sizes used in en_core_web_lg's token2vec layer.

2. Verify the new token2vec weights

After re-training, you'll get a new weights file that should have the correct dimensions. You can quickly check this by loading it in Python:

import spacy
nlp = spacy.load("./saved_en_core_web_lg")
tok2vec = nlp.get_pipe("tok2vec")
# Load the new pre-trained weights
tok2vec.from_disk("./new_model.bin")
# If no error is thrown, the dimensions match!

3. Double-check your weight loading code

Make sure you're loading the pre-trained token2vec into the correct part of the model. For spaCy 2.1.3, the right way to replace the token2vec layer for NER is:

import spacy

# Load your saved base model
nlp = spacy.load("./saved_en_core_web_lg")

# Get the tok2vec component (used by the NER pipeline)
tok2vec = nlp.get_pipe("tok2vec")
# Load the pre-trained weights
tok2vec.from_disk("./new_model.bin")

# Now you can continue fine-tuning the NER component or test its performance

Quick Notes for spaCy 2.1.x #

• Always match the base model's token2vec architecture (width, window sizes) when using spacy pre-train—this is a strict requirement in older spaCy versions.
• If you're using a custom config file for pre-training, ensure the [tok2vec] section has width = 480 and window_sizes = [1,3,5] to align with en_core_web_lg.

内容的提问来源于stack exchange，提问作者gforczz