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This package provides the compiler (rustc) and the documentation utilities rustdoc.", "tags": null, "text_prefix": true, "timestamp": 1626496060, "version": "1.50.0" }, "sacremoses": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": "MosesTokenizer in Python", "dev_url": null, "doc_source_url": null, "doc_url": null, "home": "https://github.com/alvations/sacremoses", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "LGPL-2.1-or-later", "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": null, "source_url": "https://pypi.io/packages/source/s/sacremoses/sacremoses-0.0.43.tar.gz", "subdirs": [ "noarch" ], "summary": "SacreMoses", "tags": null, "text_prefix": false, "timestamp": 1624494229, "version": "0.0.43" }, "semantic_version": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": "This small python library provides a few tools to handle SemVer in Python. 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SentencePiece implements subword units (e.g., byte-pair-encoding (BPE) [Sennrich et al.]) and unigram language model [Kudo.]) with the extension of direct training from raw sentences. 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null, "doc_url": "https://github.com/explosion/srsly", "home": "http://github.com/explosion/srsly", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "MIT", "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": "https://github.com/explosion/srsly.git", "source_url": null, "subdirs": [ "linux-64", "linux-ppc64le" ], "summary": "Modern high-performance serialization utilities for Python", "tags": null, "text_prefix": false, "timestamp": 1626510398, "version": "1.0.4" }, "tensorboard": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": "TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs.", "dev_url": "https://github.com/tensorflow/tensorboard", "doc_source_url": null, "doc_url": null, "home": "http://tensorflow.org/", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, 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Each dataset definition contains the logic necessary to download and prepare the dataset, as well as to read it into a model using the `tf.data.Dataset` API. 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In particular, it provides pre-trained SavedModels that can be reused to solve new tasks with less training time and less training data.", "dev_url": null, "doc_source_url": null, "doc_url": null, "home": "https://www.tensorflow.org/hub/", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "Apache-2.0", "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": "https://github.com/tensorflow/hub.git", "source_url": null, "subdirs": [ "noarch" ], "summary": "A library for transfer learning by reusing parts of TensorFlow models.", "tags": null, "text_prefix": true, "timestamp": 1626494916, "version": "0.10.0" }, "tensorflow-metadata": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": "TensorFlow Metadata provides standard representations for metadata that are useful when training machine learning models with TensorFlow. The metadata serialization formats include- - A schema describing tabular data (e.g., tf.Examples). - A collection of summary statistics over such datasets. - A problem statement quantifying the objectives of a model. 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As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation.", "dev_url": "https://github.com/tensorflow/probability", "doc_source_url": null, "doc_url": "https://www.tensorflow.org/probability/overview", "home": "https://www.tensorflow.org/probability/", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "Apache-2.0", "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": "https://github.com/tensorflow/probability.git", "source_url": null, "subdirs": [ "linux-64", "linux-ppc64le" ], "summary": "TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow", "tags": null, "text_prefix": false, "timestamp": 1626511685, "version": "0.12.1" }, "tensorflow-text": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": "TF.Text is a TensorFlow library of text related ops, modules, and subgraphs. The library can perform the preprocessing regularly required by text-based models, and includes other features useful for sequence modeling not provided by core TensorFlow.", "dev_url": "https://github.com/tensorflow/text", "doc_source_url": null, "doc_url": "https://github.com/tensorflow/text/tree/v2.4.1/docs", "home": "http://tensorflow.org/", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "Apache 2.0", "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": "https://github.com/tensorflow/text.git", "source_url": null, "subdirs": [ "linux-64", "linux-ppc64le" ], "summary": "TF.Text is a TensorFlow library of text related ops, modules, and subgraphs.", "tags": null, "text_prefix": false, "timestamp": 1626511550, "version": "2.4.1" }, "tensorrt": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": "NVIDIA TensorRT is a platform for high-performance deep learning inference. 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It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications.", "dev_url": "https://github.com/NVIDIA/TensorRT/", "doc_source_url": null, "doc_url": "https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html", "home": "https://developer.nvidia.com/tensorrt", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "NVIDIA Software License, Apache-2.0", "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": null, "source_url": null, "subdirs": [ "linux-64", "linux-ppc64le" ], "summary": "TensorRT is a C++ library for high performance inference on NVIDIA GPUs and deep learning accelerators.", "tags": null, "text_prefix": false, "timestamp": 1626781720, "version": "7.2.1.6" }, "thinc": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": "thinc is a Cython library for learning models with millions of parameters and dozens of classes. It drives https://spacy.io , a pipeline of very efficient NLP components. I\u2019ve only used thinc from Cython; no real Python API is currently available.", "dev_url": null, "doc_source_url": null, "doc_url": null, "home": "https://github.com/explosion/thinc/", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "MIT", "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": null, "source_url": "https://pypi.io/packages/source/t/thinc/thinc-7.4.1.tar.gz", "subdirs": [ "linux-64", "linux-ppc64le" ], "summary": "thinc: Learn super-sparse multi-class models", "tags": null, "text_prefix": false, "timestamp": 1626510487, "version": "7.4.1" }, "tokenizers": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": "Fast State-of-the-Art Tokenizers optimized for Research and Production", "dev_url": null, "doc_source_url": null, "doc_url": "https://huggingface.co/tokenizers/", "home": "https://github.com/huggingface/tokenizers", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "Apache-2.0", "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": "https://github.com/huggingface/tokenizers.git", "source_url": null, "subdirs": [ "linux-64", "linux-ppc64le" ], "summary": "Fast State-of-the-Art Tokenizers optimized for Research and Production", "tags": null, "text_prefix": false, "timestamp": 1626496981, "version": "0.9.3" }, "torchtext": { "activate.d": false, "binary_prefix": true, "deactivate.d": false, "description": "Text utilities and datasets for PyTorch", "dev_url": "https://github.com/pytorch/text/", "doc_source_url": null, "doc_url": "https://torchtext.readthedocs.io/en/latest/", "home": "https://github.com/pytorch/text", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "BSD", "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, 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