{ "info": { "author": "", "author_email": "", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Artificial Intelligence" ], "description": "

\n \n
\n
\n \"MIT\n \"Latest\n \"Build\n \"Documentation\n

\n\n--------------------------------------------------------------------------------\n\nFairseq(-py) is a sequence modeling toolkit that allows researchers and\ndevelopers to train custom models for translation, summarization, language\nmodeling and other text generation tasks.\nWe provide reference implementations of various sequence modeling papers:\n\n
List of implemented papers

\n\n- **Convolutional Neural Networks (CNN)**\n - [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/conv_lm/README.md)\n - [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md)\n - [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel)\n - [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md)\n - [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md)\n- **LightConv and DynamicConv models**\n - [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md)\n- **Long Short-Term Memory (LSTM) networks**\n - Effective Approaches to Attention-based Neural Machine Translation (Luong et al., 2015)\n- **Transformer (self-attention) networks**\n - Attention Is All You Need (Vaswani et al., 2017)\n - [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md)\n - [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md)\n - [Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018)](examples/language_model/transformer_lm/README.md)\n - [Lexically constrained decoding with dynamic beam allocation (Post & Vilar, 2018)](examples/constrained_decoding/README.md)\n - [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md)\n - [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md)\n - [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md)\n - [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md )\n - [Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)](examples/mbart/README.md)\n - [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md)\n - [Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)](examples/unsupervised_quality_estimation/README.md)\n - [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)](examples/wav2vec/README.md)\n - [Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models (Enarvi et al., 2020)](examples/pointer_generator/README.md)\n - [Linformer: Self-Attention with Linear Complexity (Wang et al., 2020)](examples/linformer/README.md)\n - [Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)](examples/criss/README.md)\n - [Deep Transformers with Latent Depth (Li et al., 2020)](examples/latent_depth/README.md)\n- **Non-autoregressive Transformers**\n - Non-Autoregressive Neural Machine Translation (Gu et al., 2017)\n - Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al. 2018)\n - Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al. 2019)\n - Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019)\n - [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md)\n- **Finetuning**\n - [Better Fine-Tuning by Reducing Representational Collapse (Aghajanyan et al. 2020)](examples/rxf/README.md)\n\n

\n\n### What's New:\n\n- October 2020: [Added R3F/R4F (Better Fine-Tuning) code](examples/rxf/README.md)\n- October 2020: [Deep Transformer with Latent Depth code released](examples/latent_depth/README.md)\n- October 2020: [Added CRISS models and code](examples/criss/README.md)\n- September 2020: [Added Linformer code](examples/linformer/README.md)\n- September 2020: [Added pointer-generator networks](examples/pointer_generator/README.md)\n- August 2020: [Added lexically constrained decoding](examples/constrained_decoding/README.md)\n- August 2020: [wav2vec2 models and code released](examples/wav2vec/README.md)\n- July 2020: [Unsupervised Quality Estimation code released](examples/unsupervised_quality_estimation/README.md)\n- May 2020: [Follow fairseq on Twitter](https://twitter.com/fairseq)\n- April 2020: [Monotonic Multihead Attention code released](examples/simultaneous_translation/README.md)\n- April 2020: [Quant-Noise code released](examples/quant_noise/README.md)\n- April 2020: [Initial model parallel support and 11B parameters unidirectional LM released](examples/megatron_11b/README.md)\n
Previous updates

\n\n- March 2020: [Byte-level BPE code released](examples/byte_level_bpe/README.md)\n- February 2020: [mBART model and code released](examples/mbart/README.md)\n- February 2020: [Added tutorial for back-translation](https://github.com/pytorch/fairseq/tree/master/examples/backtranslation#training-your-own-model-wmt18-english-german)\n- December 2019: [fairseq 0.9.0 released](https://github.com/pytorch/fairseq/releases/tag/v0.9.0)\n- November 2019: [VizSeq released (a visual analysis toolkit for evaluating fairseq models)](https://facebookresearch.github.io/vizseq/docs/getting_started/fairseq_example)\n- November 2019: [CamemBERT model and code released](examples/camembert/README.md)\n- November 2019: [BART model and code released](examples/bart/README.md)\n- November 2019: [XLM-R models and code released](examples/xlmr/README.md)\n- September 2019: [Nonautoregressive translation code released](examples/nonautoregressive_translation/README.md)\n- August 2019: [WMT'19 models released](examples/wmt19/README.md)\n- July 2019: fairseq relicensed under MIT license\n- July 2019: [RoBERTa models and code released](examples/roberta/README.md)\n- June 2019: [wav2vec models and code released](examples/wav2vec/README.md)\n\n

\n\n### Features:\n\n- multi-GPU training on one machine or across multiple machines (data and model parallel)\n- fast generation on both CPU and GPU with multiple search algorithms implemented:\n - beam search\n - Diverse Beam Search ([Vijayakumar et al., 2016](https://arxiv.org/abs/1610.02424))\n - sampling (unconstrained, top-k and top-p/nucleus)\n - lexically constrained decoding ([Post & Vilar, 2018](examples/constrained_decoding/README.md))\n- large mini-batch training even on a single GPU via delayed updates\n- mixed precision training (trains faster with less GPU memory on [NVIDIA tensor cores](https://developer.nvidia.com/tensor-cores))\n- extensible: easily register new models, criterions, tasks, optimizers and learning rate schedulers\n\nWe also provide [pre-trained models for translation and language modeling](#pre-trained-models-and-examples)\nwith a convenient `torch.hub` interface:\n```python\nen2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model')\nen2de.translate('Hello world', beam=5)\n# 'Hallo Welt'\n```\nSee the PyTorch Hub tutorials for [translation](https://pytorch.org/hub/pytorch_fairseq_translation/)\nand [RoBERTa](https://pytorch.org/hub/pytorch_fairseq_roberta/) for more examples.\n\n# Requirements and Installation\n\n* [PyTorch](http://pytorch.org/) version >= 1.5.0\n* Python version >= 3.6\n* For training new models, you'll also need an NVIDIA GPU and [NCCL](https://github.com/NVIDIA/nccl)\n* **To install fairseq** and develop locally:\n```bash\ngit clone https://github.com/pytorch/fairseq\ncd fairseq\npip install --editable ./\n\n# on MacOS:\n# CFLAGS=\"-stdlib=libc++\" pip install --editable ./\n```\n* **For faster training** install NVIDIA's [apex](https://github.com/NVIDIA/apex) library:\n```bash\ngit clone https://github.com/NVIDIA/apex\ncd apex\npip install -v --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" \\\n --global-option=\"--deprecated_fused_adam\" --global-option=\"--xentropy\" \\\n --global-option=\"--fast_multihead_attn\" ./\n```\n* **For large datasets** install [PyArrow](https://arrow.apache.org/docs/python/install.html#using-pip): `pip install pyarrow`\n* If you use Docker make sure to increase the shared memory size either with\n`--ipc=host` or `--shm-size` as command line options to `nvidia-docker run`.\n\n\n# Getting Started\n\nThe [full documentation](https://fairseq.readthedocs.io/) contains instructions\nfor getting started, training new models and extending fairseq with new model\ntypes and tasks.\n\n# Pre-trained models and examples\n\nWe provide pre-trained models and pre-processed, binarized test sets for several tasks listed below,\nas well as example training and evaluation commands.\n\n- [Translation](examples/translation/README.md): convolutional and transformer models are available\n- [Language Modeling](examples/language_model/README.md): convolutional and transformer models are available\n\nWe also have more detailed READMEs to reproduce results from specific papers:\n- [Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)](examples/criss/README.md)\n- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)](examples/wav2vec/README.md)\n- [Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)](examples/unsupervised_quality_estimation/README.md)\n- [Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)](examples/quant_noise/README.md)\n- [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md)\n- [Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)](examples/mbart/README.md)\n- [Reducing Transformer Depth on Demand with Structured Dropout (Fan et al., 2019)](examples/layerdrop/README.md)\n- [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md)\n- [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md)\n- [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md)\n- [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md)\n- [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md)\n- [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md)\n- [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md)\n- [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md)\n- [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel)\n- [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md)\n- [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md)\n- [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md)\n- [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/conv_lm/README.md)\n\n# Join the fairseq community\n\n* Twitter: https://twitter.com/fairseq\n* Facebook page: https://www.facebook.com/groups/fairseq.users\n* Google group: https://groups.google.com/forum/#!forum/fairseq-users\n\n# License\n\nfairseq(-py) is MIT-licensed.\nThe license applies to the pre-trained models as well.\n\n# Citation\n\nPlease cite as:\n\n```bibtex\n@inproceedings{ott2019fairseq,\n title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},\n author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},\n booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},\n year = {2019},\n}\n```\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/pytorch/fairseq", "keywords": "", "license": "", "maintainer": "", "maintainer_email": "", "name": "fairseq", "package_url": "https://pypi.org/project/fairseq/", "platform": "", "project_url": "https://pypi.org/project/fairseq/", "project_urls": { "Homepage": "https://github.com/pytorch/fairseq" }, "release_url": "https://pypi.org/project/fairseq/0.10.2/", "requires_dist": [ "cffi", "cython", "dataclasses", "hydra-core", "numpy", "regex", "sacrebleu (>=1.4.12)", "torch", "tqdm" ], "requires_python": "", "summary": "Facebook AI Research Sequence-to-Sequence Toolkit", "version": "0.10.2", "yanked": false, "yanked_reason": null }, "last_serial": 12507684, "releases": { "0.10.0": [ { "comment_text": "", "digests": { "md5": "ce0dbf31ca4ce48fc614b1a7a2049094", "sha256": "470ea6193d3e6f3e1b09a155b081e47d7be944b2527c2cb957388932fa9bdbc1" }, "downloads": -1, "filename": "fairseq-0.10.0.tar.gz", "has_sig": false, "md5_digest": "ce0dbf31ca4ce48fc614b1a7a2049094", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 677411, "upload_time": "2020-11-12T14:25:23", "upload_time_iso_8601": "2020-11-12T14:25:23.414497Z", "url": "https://files.pythonhosted.org/packages/dd/5f/fddba88a1478e6223241065f779e3eb547e0a4db0a16ae46a2cf92a257b9/fairseq-0.10.0.tar.gz", "yanked": false, "yanked_reason": null } ], "0.10.1": [ { "comment_text": "", "digests": { "md5": "bde2713f28c15e7c12e7eec0110e0cb3", "sha256": "12cebaf282a115baaf6f164b4effcdb30da595ce8845a8294c356c6897974d55" }, "downloads": -1, "filename": "fairseq-0.10.1-cp36-cp36m-macosx_10_9_x86_64.whl", "has_sig": false, "md5_digest": "bde2713f28c15e7c12e7eec0110e0cb3", "packagetype": "bdist_wheel", "python_version": "cp36", "requires_python": null, "size": 1133354, "upload_time": "2020-11-21T20:52:43", "upload_time_iso_8601": "2020-11-21T20:52:43.822884Z", "url": "https://files.pythonhosted.org/packages/75/8e/3b1f2088e67a1f686f05024357a2558c840f7c3927bbef7f323e22350b75/fairseq-0.10.1-cp36-cp36m-macosx_10_9_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "4fc4cfbab98c15bdc8bbabb5a79e86c2", "sha256": "319a79807b112bde3ecb81fbf7836f9a7f57588c38f16dc7b4281ff5cbdb448e" }, "downloads": -1, "filename": "fairseq-0.10.1-cp36-cp36m-manylinux1_x86_64.whl", "has_sig": false, "md5_digest": "4fc4cfbab98c15bdc8bbabb5a79e86c2", "packagetype": "bdist_wheel", "python_version": "cp36", "requires_python": null, "size": 1689593, "upload_time": "2020-11-21T20:52:45", "upload_time_iso_8601": "2020-11-21T20:52:45.227928Z", "url": "https://files.pythonhosted.org/packages/2c/da/7c7032988dade3b21ccfd5b226e50b382abfd3459129d67240bb004506ae/fairseq-0.10.1-cp36-cp36m-manylinux1_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "ac1cbd388b06572b16d5c8f1221ae1da", "sha256": "70d70f9cee2f072a3333908e690049667b1510dd956385c1a012bfe6cf10505b" }, "downloads": -1, "filename": "fairseq-0.10.1-cp37-cp37m-macosx_10_9_x86_64.whl", "has_sig": false, "md5_digest": "ac1cbd388b06572b16d5c8f1221ae1da", "packagetype": "bdist_wheel", "python_version": "cp37", "requires_python": null, "size": 1131295, "upload_time": "2020-11-21T20:52:46", "upload_time_iso_8601": "2020-11-21T20:52:46.597437Z", "url": "https://files.pythonhosted.org/packages/7c/d0/9d423ca79791bbf401a10dd3d44105c0ef8bbcf469f500a5be75ef88d79e/fairseq-0.10.1-cp37-cp37m-macosx_10_9_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "4a898af6076f728734890edf56c422c5", "sha256": "56bee96723bdcac431485f2f866c0a875eb8fa7bf08827330360c146dbc0d86b" }, "downloads": -1, "filename": "fairseq-0.10.1-cp37-cp37m-manylinux1_x86_64.whl", "has_sig": false, "md5_digest": "4a898af6076f728734890edf56c422c5", "packagetype": "bdist_wheel", "python_version": "cp37", "requires_python": null, "size": 1690087, "upload_time": "2020-11-21T20:52:48", "upload_time_iso_8601": "2020-11-21T20:52:48.295918Z", "url": "https://files.pythonhosted.org/packages/d7/a2/8883a7b699a2315465836ffdbd3ad1ef347645196093783e063539ae8462/fairseq-0.10.1-cp37-cp37m-manylinux1_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "a28bce5815b2faff49fda81bafb7a1f9", "sha256": "4a9bbb1a147f09ffe5c5739ea209fdf5693330297b5fd3e9f54343af971510b6" }, "downloads": -1, "filename": "fairseq-0.10.1-cp38-cp38-macosx_10_9_x86_64.whl", "has_sig": false, "md5_digest": "a28bce5815b2faff49fda81bafb7a1f9", "packagetype": "bdist_wheel", "python_version": "cp38", "requires_python": null, "size": 1131784, "upload_time": "2020-11-21T20:52:49", "upload_time_iso_8601": "2020-11-21T20:52:49.629953Z", "url": "https://files.pythonhosted.org/packages/e0/6c/3339047d0746273ab98e1d9e4b1f5dbf2d2291c2db982bac49a3b78e4010/fairseq-0.10.1-cp38-cp38-macosx_10_9_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "e1256bbefca973fd16ef3bbd5ace1e0c", "sha256": "f4eff34e1fceca214a8d94b42282a6b33bd82dc376d4f2da9334ffe4c8bd92f5" }, "downloads": -1, "filename": "fairseq-0.10.1-cp38-cp38-manylinux1_x86_64.whl", "has_sig": false, "md5_digest": "e1256bbefca973fd16ef3bbd5ace1e0c", "packagetype": "bdist_wheel", "python_version": "cp38", "requires_python": null, "size": 1695506, "upload_time": "2020-11-21T20:52:51", "upload_time_iso_8601": "2020-11-21T20:52:51.011432Z", "url": "https://files.pythonhosted.org/packages/62/3c/a5aa58af1c48b539ee1eddbcf999647b2c74c7257cc4296c7aed6592e668/fairseq-0.10.1-cp38-cp38-manylinux1_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "0a26502a647bd7c6800f4f9a64c19454", "sha256": "05b9e6bea4fc974b33e0ee5c154ec4069c6996be30c53d3ea8dd6d13113170f2" }, "downloads": -1, "filename": "fairseq-0.10.1.tar.gz", "has_sig": false, "md5_digest": "0a26502a647bd7c6800f4f9a64c19454", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 924991, "upload_time": "2020-11-21T20:52:52", "upload_time_iso_8601": "2020-11-21T20:52:52.382779Z", "url": "https://files.pythonhosted.org/packages/52/00/8668312a50a607d1ceb4a6f0804662a04f7b029857679f518a2ec4777d75/fairseq-0.10.1.tar.gz", "yanked": false, "yanked_reason": null } ], "0.10.2": [ { "comment_text": "", "digests": { "md5": "1453b57123df56531569b262cdd3743e", "sha256": "638ab9b4973fb8443cab6a62a0ccb4b02a814eb6aafbcf490e6df47391236a8e" }, "downloads": -1, "filename": "fairseq-0.10.2-cp36-cp36m-macosx_10_9_x86_64.whl", "has_sig": false, "md5_digest": "1453b57123df56531569b262cdd3743e", "packagetype": "bdist_wheel", "python_version": "cp36", "requires_python": null, "size": 1133253, "upload_time": "2021-01-05T20:26:45", "upload_time_iso_8601": "2021-01-05T20:26:45.101637Z", "url": "https://files.pythonhosted.org/packages/15/36/538c62c2815af17ae23e4346ce9fc491977e8913417d3289da7519863b41/fairseq-0.10.2-cp36-cp36m-macosx_10_9_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "329f48a04827957176aba89070a7390f", "sha256": "8d3fb301f182b0168cac93327638fa4c044658fac16eb39dea18a1b9b471fc40" }, "downloads": -1, "filename": "fairseq-0.10.2-cp36-cp36m-manylinux1_x86_64.whl", "has_sig": false, "md5_digest": "329f48a04827957176aba89070a7390f", "packagetype": "bdist_wheel", "python_version": "cp36", "requires_python": null, "size": 1689506, "upload_time": "2021-01-05T20:26:46", "upload_time_iso_8601": "2021-01-05T20:26:46.715421Z", "url": "https://files.pythonhosted.org/packages/61/7b/2c90e007d737f4a2b7cd5066ac3a3d88acb2ce765972a61c308914c95568/fairseq-0.10.2-cp36-cp36m-manylinux1_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "60c4f5888d39fe717c27935310156796", "sha256": "8ecd29be6d769187d06b42b4d929fe0ad799a79614343465be1b461acfa0acf3" }, "downloads": -1, "filename": "fairseq-0.10.2-cp37-cp37m-macosx_10_9_x86_64.whl", "has_sig": false, "md5_digest": "60c4f5888d39fe717c27935310156796", "packagetype": "bdist_wheel", "python_version": "cp37", "requires_python": null, "size": 1131199, "upload_time": "2021-01-05T20:26:48", "upload_time_iso_8601": "2021-01-05T20:26:48.534800Z", "url": "https://files.pythonhosted.org/packages/69/65/9b7315be536fbb8e5233c062ba61746452f2628a86863ef36684482ec99f/fairseq-0.10.2-cp37-cp37m-macosx_10_9_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "2ff2255abe046b766f62fd4f25443b4f", "sha256": "b9c40a0c17003ef7b1bd971813f48e913bf8ee8f9517565b2e63c1ba3d3a0d23" }, "downloads": -1, "filename": "fairseq-0.10.2-cp37-cp37m-manylinux1_x86_64.whl", "has_sig": false, "md5_digest": "2ff2255abe046b766f62fd4f25443b4f", "packagetype": "bdist_wheel", "python_version": "cp37", "requires_python": null, "size": 1690047, "upload_time": "2021-01-05T20:26:50", "upload_time_iso_8601": "2021-01-05T20:26:50.331229Z", "url": "https://files.pythonhosted.org/packages/15/ab/92c6efb05ffdfe16fbdc9e463229d9af8c3b74dc943ed4b4857a87b223c2/fairseq-0.10.2-cp37-cp37m-manylinux1_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "e6ca19858c8894bd87ba773f01d1d2f7", "sha256": "67fe259dbe0c4c17f65bc7c077b9cf1437eae3819022e84fc2e6e3bfe04746c2" }, "downloads": -1, "filename": "fairseq-0.10.2-cp38-cp38-macosx_10_9_x86_64.whl", "has_sig": false, "md5_digest": "e6ca19858c8894bd87ba773f01d1d2f7", "packagetype": "bdist_wheel", "python_version": "cp38", "requires_python": null, "size": 1131689, "upload_time": "2021-01-05T20:26:51", "upload_time_iso_8601": "2021-01-05T20:26:51.782502Z", "url": "https://files.pythonhosted.org/packages/14/70/ac2c867b1eecaf5b9b6096165646a41954f3d6f9c9f8ffb906e344a905fa/fairseq-0.10.2-cp38-cp38-macosx_10_9_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "9e44cb2a8d2b3ff7f4572e02ca588c0a", "sha256": "e807a39a44e93d830e8372a05aa27315b0c18499748f3862f262b7e3fcda646d" }, "downloads": -1, "filename": "fairseq-0.10.2-cp38-cp38-manylinux1_x86_64.whl", "has_sig": false, "md5_digest": "9e44cb2a8d2b3ff7f4572e02ca588c0a", "packagetype": "bdist_wheel", "python_version": "cp38", "requires_python": null, "size": 1695483, "upload_time": "2021-01-05T20:26:53", "upload_time_iso_8601": "2021-01-05T20:26:53.540909Z", "url": "https://files.pythonhosted.org/packages/88/15/1b09022f20f971eae992497bdde69dc11b3e8d586c95e93e43a6842979c5/fairseq-0.10.2-cp38-cp38-manylinux1_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "705821d37b4907db9409f7e1315c4e78", "sha256": "45b90d8ccc3f5a4623a523fe2d0465f54413d03fd1ec9a9d7af0461148ca1a68" }, "downloads": -1, "filename": "fairseq-0.10.2.tar.gz", "has_sig": false, "md5_digest": "705821d37b4907db9409f7e1315c4e78", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 938072, "upload_time": "2021-01-05T20:26:55", "upload_time_iso_8601": "2021-01-05T20:26:55.064075Z", "url": "https://files.pythonhosted.org/packages/02/59/1f466d82d64482e2809a167e0067979c23057b3752e668537d5ff24453c9/fairseq-0.10.2.tar.gz", "yanked": false, "yanked_reason": null } ], "0.6.1": [ { "comment_text": "", "digests": { "md5": "42fa1c58ff11a36f9ff500e0f08909ce", "sha256": "7133d1b163805535e13e91068c03c4b9cddec327a8748df27d90560215f3f5a6" }, "downloads": -1, "filename": "fairseq-0.6.1.tar.gz", "has_sig": false, "md5_digest": "42fa1c58ff11a36f9ff500e0f08909ce", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 132864, "upload_time": "2019-02-09T05:46:51", "upload_time_iso_8601": "2019-02-09T05:46:51.246071Z", "url": "https://files.pythonhosted.org/packages/cb/56/2032410e608c310418fbe6be6a89cd23d1b0e392872063d5464611b63267/fairseq-0.6.1.tar.gz", "yanked": false, "yanked_reason": null } ], "0.6.2": [ { "comment_text": "", "digests": { "md5": "a52ca46bd35d83e81a8e2e7bed0045d8", "sha256": "4ef01eb5f4e1731e16ac3ea06122ca1526f768ab46a218a20fd972687f46cbd7" }, "downloads": -1, "filename": "fairseq-0.6.2.tar.gz", "has_sig": false, "md5_digest": "a52ca46bd35d83e81a8e2e7bed0045d8", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 139260, "upload_time": "2019-03-15T17:36:05", "upload_time_iso_8601": "2019-03-15T17:36:05.684525Z", "url": "https://files.pythonhosted.org/packages/d7/b1/bb10d50b5fb0a030ddbeb049c8df66137f3a137e42d6d2474e73aa878a51/fairseq-0.6.2.tar.gz", "yanked": false, "yanked_reason": null } ], "0.7.1": [ { "comment_text": "", "digests": { "md5": "84f1fb0a46258991bfd0cf2cbeac0bf1", "sha256": "559116342b3c11f948ea29eea0d35a82668351c83c058d77800bb88aa6151842" }, "downloads": -1, "filename": "fairseq-0.7.1.tar.gz", "has_sig": false, "md5_digest": "84f1fb0a46258991bfd0cf2cbeac0bf1", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 186342, "upload_time": "2019-06-20T15:21:16", "upload_time_iso_8601": "2019-06-20T15:21:16.345713Z", "url": "https://files.pythonhosted.org/packages/97/b4/b37d9ef01891ed1884f7d969d6b2ba4ac3e1adcada34dda3e39c4caac9b9/fairseq-0.7.1.tar.gz", "yanked": false, "yanked_reason": null } ], "0.7.2": [ { "comment_text": "", "digests": { "md5": "4c3624eeb4a45027975d06b9a3bc59ee", "sha256": "66675264017ed345da5d085325189705f091a3da83896049d4f37508c6d33426" }, "downloads": -1, "filename": "fairseq-0.7.2.tar.gz", "has_sig": false, "md5_digest": "4c3624eeb4a45027975d06b9a3bc59ee", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 190759, "upload_time": "2019-07-19T13:43:26", "upload_time_iso_8601": "2019-07-19T13:43:26.118164Z", "url": "https://files.pythonhosted.org/packages/1c/13/41fb03306f9e50581210d2fb24f2f056f700f9ffdddb0f7734c7bda5d715/fairseq-0.7.2.tar.gz", "yanked": false, "yanked_reason": null } ], "0.8.0": [ { "comment_text": "", "digests": { "md5": "b3fcd8aeb7a7420636040b54fd2c8d47", "sha256": "55dfedb630ba76ac6c25bf443fd624ce78e097b4820fdc3b1c6d2b153e9f212e" }, "downloads": -1, "filename": "fairseq-0.8.0.tar.gz", "has_sig": false, "md5_digest": "b3fcd8aeb7a7420636040b54fd2c8d47", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 216335, "upload_time": "2019-08-14T12:35:17", "upload_time_iso_8601": "2019-08-14T12:35:17.563948Z", "url": "https://files.pythonhosted.org/packages/62/19/a71af3ea3bdf7c2fd66c8076a3be090147f700e3e513b0b3b11d80d97fe3/fairseq-0.8.0.tar.gz", "yanked": false, "yanked_reason": null } ], "0.9.0": [ { "comment_text": "", "digests": { "md5": "174a84c432d209995e8f0bcfff93cf78", "sha256": "61206358b79f325ea0b46cfd8c95cdb81bfbcfb43cf12b47d1d5124ce7321d3b" }, "downloads": -1, "filename": "fairseq-0.9.0.tar.gz", "has_sig": false, "md5_digest": "174a84c432d209995e8f0bcfff93cf78", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 306103, "upload_time": "2019-12-04T14:33:03", "upload_time_iso_8601": "2019-12-04T14:33:03.217666Z", "url": "https://files.pythonhosted.org/packages/67/bf/de299e082e7af010d35162cb9a185dc6c17db71624590f2f379aeb2519ff/fairseq-0.9.0.tar.gz", "yanked": false, "yanked_reason": null } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "1453b57123df56531569b262cdd3743e", "sha256": "638ab9b4973fb8443cab6a62a0ccb4b02a814eb6aafbcf490e6df47391236a8e" }, "downloads": -1, "filename": "fairseq-0.10.2-cp36-cp36m-macosx_10_9_x86_64.whl", "has_sig": false, "md5_digest": "1453b57123df56531569b262cdd3743e", "packagetype": "bdist_wheel", "python_version": "cp36", "requires_python": null, "size": 1133253, "upload_time": "2021-01-05T20:26:45", "upload_time_iso_8601": "2021-01-05T20:26:45.101637Z", "url": "https://files.pythonhosted.org/packages/15/36/538c62c2815af17ae23e4346ce9fc491977e8913417d3289da7519863b41/fairseq-0.10.2-cp36-cp36m-macosx_10_9_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "329f48a04827957176aba89070a7390f", "sha256": "8d3fb301f182b0168cac93327638fa4c044658fac16eb39dea18a1b9b471fc40" }, "downloads": -1, "filename": "fairseq-0.10.2-cp36-cp36m-manylinux1_x86_64.whl", "has_sig": false, "md5_digest": "329f48a04827957176aba89070a7390f", "packagetype": "bdist_wheel", "python_version": "cp36", "requires_python": null, "size": 1689506, "upload_time": "2021-01-05T20:26:46", "upload_time_iso_8601": "2021-01-05T20:26:46.715421Z", "url": "https://files.pythonhosted.org/packages/61/7b/2c90e007d737f4a2b7cd5066ac3a3d88acb2ce765972a61c308914c95568/fairseq-0.10.2-cp36-cp36m-manylinux1_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "60c4f5888d39fe717c27935310156796", "sha256": "8ecd29be6d769187d06b42b4d929fe0ad799a79614343465be1b461acfa0acf3" }, "downloads": -1, "filename": "fairseq-0.10.2-cp37-cp37m-macosx_10_9_x86_64.whl", "has_sig": false, "md5_digest": "60c4f5888d39fe717c27935310156796", "packagetype": "bdist_wheel", "python_version": "cp37", "requires_python": null, "size": 1131199, "upload_time": "2021-01-05T20:26:48", "upload_time_iso_8601": "2021-01-05T20:26:48.534800Z", "url": "https://files.pythonhosted.org/packages/69/65/9b7315be536fbb8e5233c062ba61746452f2628a86863ef36684482ec99f/fairseq-0.10.2-cp37-cp37m-macosx_10_9_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "2ff2255abe046b766f62fd4f25443b4f", "sha256": "b9c40a0c17003ef7b1bd971813f48e913bf8ee8f9517565b2e63c1ba3d3a0d23" }, "downloads": -1, "filename": "fairseq-0.10.2-cp37-cp37m-manylinux1_x86_64.whl", "has_sig": false, "md5_digest": "2ff2255abe046b766f62fd4f25443b4f", "packagetype": "bdist_wheel", "python_version": "cp37", "requires_python": null, "size": 1690047, "upload_time": "2021-01-05T20:26:50", "upload_time_iso_8601": "2021-01-05T20:26:50.331229Z", "url": "https://files.pythonhosted.org/packages/15/ab/92c6efb05ffdfe16fbdc9e463229d9af8c3b74dc943ed4b4857a87b223c2/fairseq-0.10.2-cp37-cp37m-manylinux1_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "e6ca19858c8894bd87ba773f01d1d2f7", "sha256": "67fe259dbe0c4c17f65bc7c077b9cf1437eae3819022e84fc2e6e3bfe04746c2" }, "downloads": -1, "filename": "fairseq-0.10.2-cp38-cp38-macosx_10_9_x86_64.whl", "has_sig": false, "md5_digest": "e6ca19858c8894bd87ba773f01d1d2f7", "packagetype": "bdist_wheel", "python_version": "cp38", "requires_python": null, "size": 1131689, "upload_time": "2021-01-05T20:26:51", "upload_time_iso_8601": "2021-01-05T20:26:51.782502Z", "url": "https://files.pythonhosted.org/packages/14/70/ac2c867b1eecaf5b9b6096165646a41954f3d6f9c9f8ffb906e344a905fa/fairseq-0.10.2-cp38-cp38-macosx_10_9_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "9e44cb2a8d2b3ff7f4572e02ca588c0a", "sha256": "e807a39a44e93d830e8372a05aa27315b0c18499748f3862f262b7e3fcda646d" }, "downloads": -1, "filename": "fairseq-0.10.2-cp38-cp38-manylinux1_x86_64.whl", "has_sig": false, "md5_digest": "9e44cb2a8d2b3ff7f4572e02ca588c0a", "packagetype": "bdist_wheel", "python_version": "cp38", "requires_python": null, "size": 1695483, "upload_time": "2021-01-05T20:26:53", "upload_time_iso_8601": "2021-01-05T20:26:53.540909Z", "url": "https://files.pythonhosted.org/packages/88/15/1b09022f20f971eae992497bdde69dc11b3e8d586c95e93e43a6842979c5/fairseq-0.10.2-cp38-cp38-manylinux1_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": "", "digests": { "md5": "705821d37b4907db9409f7e1315c4e78", "sha256": "45b90d8ccc3f5a4623a523fe2d0465f54413d03fd1ec9a9d7af0461148ca1a68" }, "downloads": -1, "filename": "fairseq-0.10.2.tar.gz", "has_sig": false, "md5_digest": "705821d37b4907db9409f7e1315c4e78", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 938072, "upload_time": "2021-01-05T20:26:55", "upload_time_iso_8601": "2021-01-05T20:26:55.064075Z", "url": "https://files.pythonhosted.org/packages/02/59/1f466d82d64482e2809a167e0067979c23057b3752e668537d5ff24453c9/fairseq-0.10.2.tar.gz", "yanked": false, "yanked_reason": null } ], "vulnerabilities": [] }