dropwise-metrics 0.1.1

TorchMetrics-compatible predictive uncertainty metrics using MC Dropout

Dropwise-Metrics

Dropwise-Metrics is a lightweight TorchMetrics-compatible toolkit for performing Monte Carlo Dropout–based uncertainty estimation in Transformers. It enables confidence-aware decision making by revealing how certain a model is about its predictions — packaged as plug-and-play PyTorch Metric classes.

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Features

• Enable dropout during inference for Bayesian-like uncertainty estimation
• Compute predictive entropy, confidence, and per-class standard deviation
• Modular support for classification, QA, token tagging, and regression
• Works seamlessly with Hugging Face Transformers and PyTorch
• TorchMetrics-compatible: .update() + .compute()
• Supports batch inference, CPU/GPU, and customizable num_passes
• Cleanly packaged and extensible for research or production

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Supported Tasks

• sequence-classification — e.g. distilbert-base-uncased-finetuned-sst-2-english
• token-classification — e.g. dslim/bert-base-NER
• question-answering — e.g. deepset/bert-base-cased-squad2
• regression — e.g. roberta-base with a custom head

Note: Your model must contain dropout layers for MC sampling to work (most Hugging Face models do).

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Installation

pip install dropwise-metrics

Or install from source:

git clone https://github.com/aryanator/dropwise-metrics.git
cd dropwise-metrics
pip install -e .

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Example Usage (Metric Style)

Sequence Classification

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from dropwise_metrics.metrics.entropy import PredictiveEntropyMetric

model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")

metric = PredictiveEntropyMetric(model, tokenizer, task_type="sequence-classification", num_passes=20)
metric.update(["The movie was fantastic!", "Awful experience."])
results = metric.compute()

print(results[0])

Token Classification (NER)

from transformers import AutoModelForTokenClassification, AutoTokenizer
from dropwise_metrics.metrics.entropy import PredictiveEntropyMetric

model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")

metric = PredictiveEntropyMetric(model, tokenizer, task_type="token-classification", num_passes=15)
metric.update(["Hugging Face is based in New York City."])
results = metric.compute()

print(results[0]['token_predictions'])

Question Answering

from transformers import AutoModelForQuestionAnswering, AutoTokenizer
from dropwise_metrics.metrics.entropy import PredictiveEntropyMetric

model = AutoModelForQuestionAnswering.from_pretrained("deepset/bert-base-cased-squad2")
tokenizer = AutoTokenizer.from_pretrained("deepset/bert-base-cased-squad2")

question = "Where is Hugging Face based?"
context = "Hugging Face Inc. is a company based in New York City."
qa_input = f"{question} [SEP] {context}"

metric = PredictiveEntropyMetric(model, tokenizer, task_type="question-answering", num_passes=10)
metric.update([qa_input])
results = metric.compute()

print(results[0]['answer'])

Regression

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from dropwise_metrics.metrics.entropy import PredictiveEntropyMetric

model = AutoModelForSequenceClassification.from_pretrained("roberta-base", num_labels=1)
tokenizer = AutoTokenizer.from_pretrained("roberta-base")

metric = PredictiveEntropyMetric(model, tokenizer, task_type="regression", num_passes=20)
metric.update(["The child is very young."])
results = metric.compute()

print(results[0]['predicted_score'], "+/-", results[0]['uncertainty'])

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Output Dictionary (per sample)

Common fields returned:

• predicted_class: Most probable class (classification)
• predicted_score: Scalar prediction (regression)
• confidence: Highest softmax probability
• entropy: Predictive entropy (lower = more confident)
• std_dev: Per-class standard deviation
• probs: Raw softmax probabilities
• margin: Confidence gap between top-2 classes
• answer: Predicted span (question answering only)
• token_predictions: Per-token predictions (NER only)

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Run Tests

python test_entropy.py

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Folder Structure

dropwise_metrics/
├── base.py
├── metrics/
│   └── entropy.py
├── tasks/
│   ├── __init__.py
│   ├── sequence_classification.py
│   ├── token_classification.py
│   ├── question_answering.py
│   └── regression.py

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License

MIT License

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