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Change-Id: Iedab56f6c2bdde12e654ba67695cd996122bdb0b --- gnu/packages/machine-learning.scm | 54 +++++++++++++++++++++++++++++++ 1 file changed, 54 insertions(+) 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