22 lines
1.1 KiB
Plaintext
22 lines
1.1 KiB
Plaintext
# Optional: only needed when classifier.mode: local_encoder is configured.
|
|
# Not part of requirements.txt deliberately -- this project's dependency
|
|
# tree is pinned and bumped deliberately, and torch is large enough (and
|
|
# CPU/CUDA-wheel-specific enough) to warrant staying out of every install
|
|
# rather than every deployment paying for it whether or not the mode is used.
|
|
#
|
|
# CPU install (the classifier.encoder.device: cpu default):
|
|
# pip install -r requirements-encoder.txt
|
|
#
|
|
# CUDA install: replace the torch line with the CUDA wheel index per
|
|
# https://pytorch.org/get-started/locally/ -- the exact index URL is
|
|
# CUDA-version-specific and changes upstream, so it is not pinned here.
|
|
transformers==4.57.1
|
|
torch==2.9.1
|
|
|
|
# TRAINING-time only, for scripts/train_encoder_head.py: fitting and
|
|
# Platt-calibrating the logistic-regression head on frozen embeddings. The
|
|
# serving side (local_encoder._TrainableHead) reads the committed
|
|
# coefficients JSON in pure Python, so deployments that never retrain the
|
|
# head need neither scikit-learn nor its scipy/joblib pulls.
|
|
scikit-learn==1.9.1
|