为何同一标注器程序用相同数据多次运行准确率不同？

Hey there! This is a super common gotcha in NLP and machine learning projects—let’s break down the most likely reasons and how to fix them:

1. Unfixed Random Seeds #

Most ML libraries (scikit-learn, TensorFlow, PyTorch, etc.) rely on random number generation for core operations like:

• Initializing model weights randomly
• Shuffling training batch orders
• Applying regularization techniques like dropout
• Even some feature engineering steps that involve random sampling

If you don’t explicitly set a fixed random seed at the start of your script, every run will use a different seed. This leads to different model initializations and training paths, resulting in varying accuracy scores.

Quick Fix: Add seed initialization at the top of your code #

import random
import numpy as np

# Fix seeds for Python's built-in random and NumPy
random.seed(42)
np.random.seed(42)

# If using scikit-learn, fix shuffle seeds when handling data
from sklearn.utils import shuffle
X_train, y_train = shuffle(X_train, y_train, random_state=42)

# If using PyTorch (for GPU/CPU consistency)
import torch
torch.manual_seed(42)
torch.cuda.manual_seed_all(42)

# If using TensorFlow/Keras
import tensorflow as tf
tf.random.set_seed(42)

2. Randomness in Training Logic #

Check if your training pipeline includes inherently random steps that aren’t controlled:

• Dropout layers: These randomly deactivate neurons during training (great for regularization, but introduces randomness unless disabled during evaluation)
• Stochastic optimizers: Algorithms like SGD or Adam sample batches randomly—even with fixed data, batch order changes can shift model convergence
• Data augmentation: If you’re adding random tweaks to training data (e.g., random word substitutions) without fixing the seed, the model sees different data each run

Fixes: #

• Disable dropout when calculating accuracy on your dev set
• Use a fixed seed for batch shuffling during training
• Lock the seed for any data augmentation operations

3. Undeterministic Data Processing #

Take a close look at your transform_to_dataset function and feature generation code:

• Are you using random sampling to create features?
• Is there shuffling happening in this step without a fixed seed?
• Do you rely on non-deterministic behavior (like unordered dictionary keys in older Python versions)?

Fixes: #

• Audit your feature pipeline for randomness and add seeds where needed
• For Python versions <3.7, avoid relying on dictionary key order for feature generation (use ordered dictionaries if necessary)

4. Model-Specific Non-Determinism #

Some models have built-in randomness by design:

• Random forests or gradient boosting machines that sample data for each tree
• Neural networks with asynchronous training (common in multi-GPU setups)

Fix: #

When initializing your model, set the random_state (or equivalent) parameter. For example, in scikit-learn:

from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(random_state=42)

Start with fixing all random seeds first—that’s the most impactful fix for this issue. Once you lock those down, you should see consistent accuracy scores across runs!

内容的提问来源于stack exchange，提问作者selubamih