@@ -5846,6 +5846,66 @@ (define-public python-tokenizers
tokenizers, @code{rust-tokenizers}.")
(license license:asl2.0)))
+(define-public python-transformers
+ (package
+ (name "python-transformers")
+ (version "4.44.2")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (pypi-uri "transformers" version))
+ (sha256
+ (base32 "09h84wqqk2bgi4vr9d1m3dsliard99l53n96wic405gfjb61gain"))))
+ (build-system pyproject-build-system)
+ ;; The imported package contains ~60 more inputs, but they don't seem
+ ;; necessary to build a minimal version of the package.
+ (propagated-inputs
+ (list python-filelock
+ python-huggingface-hub
+ python-numpy
+ python-pytorch
+ python-pyyaml
+ python-regex
+ python-requests
+ python-safetensors
+ python-tokenizers
+ python-tqdm
+ tensorflow))
+ (home-page "https://github.com/huggingface/transformers")
+ (synopsis "Machine Learning for PyTorch and TensorFlow")
+ (description
+ "This package provides easy download of thousands of pretrained models to
+perform tasks on different modalities such as text, vision, and audio.
+
+These models can be applied on:
+@itemize
+@item Text, for tasks like text classification, information extraction,
+question answering, summarization, translation, and text generation, in over
+100 languages.
+@item Images, for tasks like image classification, object detection, and
+segmentation.
+@item Audio, for tasks like speech recognition and audio classification.
+@end itemize
+
+Transformer models can also perform tasks on several modalities combined, such
+as table question answering, optical character recognition, information
+extraction from scanned documents, video classification, and visual question
+answering.
+
+This package provides APIs to quickly download and use those pretrained models
+on a given text, fine-tune them on your own datasets and then share them with
+the community on our model hub. At the same time, each python module defining
+an architecture is fully standalone and can be modified to enable quick
+research experiments.
+
+Transformers is backed by the three most popular deep learning libraries —
+Jax, PyTorch and TensorFlow — with a seamless integration between them. It's
+straightforward to train your models with one before loading them for
+inference with the other.
+
+Note: This version doesn't support integration with JAX.")
+ (license license:asl2.0)))
+
(define-public python-hmmlearn
(package
(name "python-hmmlearn")