Dropwise

Dropwise[https://pypi.org/project/dropwise/] is a lightweight PyTorch/HuggingFace wrapper 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 — all with just a few lines of code.

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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
• ✅ Supports batch inference, CPU/GPU, and customizable num_passes
• ✅ Cleanly packaged and extensible for research or production

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🤖 Supported Tasks

Task Type	Example Model
sequence-classification	distilbert-base-uncased-finetuned-sst-2-english
token-classification	dslim/bert-base-NER
question-answering	deepset/bert-base-cased-squad2
regression	roberta-base (with custom regression head)

⚠️ Your model must contain dropout layers for MC sampling to work (most HF models do).

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📦 Installation

pip install dropwise

Or install from source:

git clone https://github.com/aryanator01/dropwise.git
cd dropwise
pip install -e .

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🧠 Example Usage (Per Task)

📘 Sequence Classification

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from dropwise.predictor import DropwisePredictor

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

predictor = DropwisePredictor(model, tokenizer, task_type="sequence-classification", num_passes=20)
results = predictor(["The movie was fantastic!"])

print(results[0])

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🏷️ Token Classification (NER)

from transformers import AutoModelForTokenClassification, AutoTokenizer
from dropwise.predictor import DropwisePredictor

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

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

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

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❓ Question Answering

from transformers import AutoModelForQuestionAnswering, AutoTokenizer
from dropwise.predictor import DropwisePredictor

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}"

predictor = DropwisePredictor(model, tokenizer, task_type="question-answering", num_passes=10)
results = predictor([qa_input])

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

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📈 Regression

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from dropwise.predictor import DropwisePredictor

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

predictor = DropwisePredictor(model, tokenizer, task_type="regression", num_passes=20)
results = predictor(["The child is very young."])

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

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📊 Output Dictionary (per sample)

Field	Description
predicted_class	Index of most probable class (classification)
predicted_score	Scalar prediction (regression only)
confidence	Highest softmax probability
entropy	Predictive entropy (higher = less confident)
std_dev	Standard deviation across MC passes
probs	Class-wise softmax probabilities
margin	Confidence gap between top-2 predictions
answer	Predicted answer span (QA only)
token_predictions	List of per-token dicts (token classification)

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🧪 Run Tests

python tests/test_predictor.py

Covers all task types with preloaded models.

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📂 Folder Structure

dropwise/
├── predictor.py
├── tasks/
│   ├── __init__.py
│   ├── sequence_classification.py
│   ├── token_classification.py
│   ├── question_answering.py
│   └── regression.py
tests/
└── test_predictor.py

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📝 License

MIT License

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Built with ❤️ for robust, explainable, uncertainty-aware AI systems.