(address . guix-patches@gnu.org)(address . ngraves@ngraves.fr)
20240908000927.29091-1-ngraves@ngraves.fr
* gnu/packages/machine-learning.scm (python-sentence-transformers): New variable.
Change-Id: Iedab56f6c2bdde12e654ba67695cd996122bdb0b
---
gnu/packages/machine-learning.scm | 54 +++++++++++++++++++++++++++++++
1 file changed, 54 insertions(+)
Toggle diff (67 lines)
diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm
index 42842d7d61..b2da07e8f0 100644
--- a/gnu/packages/machine-learning.scm
+++ b/gnu/packages/machine-learning.scm
@@ -1239,6 +1239,60 @@ (define-public python-sentencepiece
unsupervised text tokenizer.")
(license license:asl2.0)))
+(define-public python-sentence-transformers
+ (package
+ (name "python-sentence-transformers")
+ (version "3.0.1")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (pypi-uri "sentence_transformers" version))
+ (sha256
+ (base32 "1xmzbyrlp6wa7adf42n67c544db17nz95b10ri603lf4gi9jqgca"))))
+ (build-system pyproject-build-system)
+ (arguments
+ (list
+ #:test-flags `(list
+ ;; Missing fixture / train or test data.
+ ;; Requires internet access.
+ "--ignore=tests/test_sentence_transformer.py"
+ "--ignore=tests/test_train_stsb.py"
+ "--ignore=tests/test_compute_embeddings.py"
+ "--ignore=tests/test_cross_encoder.py"
+ "--ignore=tests/test_model_card_data.py"
+ "--ignore=tests/test_multi_process.py"
+ "--ignore=tests/test_pretrained_stsb.py"
+ "-k" ,(string-append
+ "not test_LabelAccuracyEvaluator"
+ " and not test_ParaphraseMiningEvaluator"
+ " and not test_cmnrl_same_grad"
+ " and not test_paraphrase_mining"
+ " and not test_simple_encode"))))
+ (propagated-inputs (list python-huggingface-hub
+ python-numpy
+ python-pillow
+ python-scikit-learn
+ python-scipy
+ python-pytorch
+ python-tqdm
+ python-transformers))
+ (native-inputs (list python-pytest))
+ (home-page "https://www.SBERT.net")
+ (synopsis "Multilingual text embeddings")
+ (description "This framework provides an easy method to compute dense
+vector representations for sentences, paragraphs, and images. The models are
+based on transformer networks like BERT / RoBERTa / XLM-RoBERTa and achieve
+state-of-the-art performance in various tasks. Text is embedded in vector
+space such that similar text are closer and can efficiently be found using
+cosine similarity.
+
+This package provides easy access to pretrained models for more than 100
+languages, fine-tuned for various use-cases.
+
+Further, this framework allows an easy fine-tuning of custom embeddings
+models, to achieve maximal performance on your specific task.")
+ (license license:asl2.0)))
+
(define-public python-spacy-legacy
(package
(name "python-spacy-legacy")
--
2.45.2