official/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/common/__init__.py,sha256=Y1MXbvwHDSF4QLfTyTEgRhuZki6TOh60KIXQIeBa39A,610
official/common/dataset_fn.py,sha256=kxFOHiIGNF6jYcbRa9WGv8OkF5cj48pA3E-k2uFnKX0,1712
official/common/distribute_utils.py,sha256=n01P17FneiKhTZSdyStrIVdGOVJ-keZh1muBWZRfT8g,8593
official/common/distribute_utils_test.py,sha256=IvfSvXmnQXz2PHlNaCuz_2jH06RfXL_CtT1MN3wUoJ4,2222
official/common/flags.py,sha256=vAwGVygzyGdjtNTM6_GpuJTHeqdNxPppVyiA6SyXYbA,4166
official/common/registry_imports.py,sha256=8Tt98MKozlG9_IRqmvCzNSTgjMSajbaHs6rW87aaXiY,848
official/common/streamz_counters.py,sha256=REmVzyGFh4d6Bt7qDq-4iPkyvAPltEkWlWjcaK_I0wc,1057
official/core/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/core/actions.py,sha256=fD-xof6Ef_ZUmVFC4jl9j85JWXqGsH1xt_c6NOSEmS8,7902
official/core/actions_test.py,sha256=cpeRI5qBG4gj9SbbqSRIFTw57FkDfd-uommrScTdhNA,3627
official/core/base_task.py,sha256=cWFfBpLfcXN2AFG7vdThDkk3yGU0hAW8LuKsoYrLKbo,11910
official/core/base_trainer.py,sha256=97pRSgH4c3DqDYaMw20A7S3QM9Y6k4pwOpGPLZHzals,17707
official/core/base_trainer_test.py,sha256=ymiinqnGEwauIdyK3vZ8j4mHvlMu28DUQ7298g_ObRA,13804
official/core/config_definitions.py,sha256=nwe77typBgUm-NLaaIoz7l-fS_afM7XrDdgrPOMafRM,11871
official/core/exp_factory.py,sha256=0Ppy15dHNv1xWVI6Bbf8GMW24NyhJj8lGPSiUdlX8nE,1115
official/core/export_base.py,sha256=Wo4q4rLDBuXL0s1VesywW-UrW3FpJUQQi1EAXVwfOMc,5090
official/core/export_base_test.py,sha256=davNlPay7LNUesRff3hrZ3VNclvnfxUfbNTelZgeuBQ,4153
official/core/input_reader.py,sha256=ydwO-LBKL_YddJFX62azKkzadm43cdDU1wHo8DSvdf4,19827
official/core/registry.py,sha256=yqDXJwM5onr2EhzLIJ9iTKmUffEu0WKB2DBgiqSEf08,3949
official/core/registry_test.py,sha256=ftKLS6H96BH6w8oN2VGfgl_QcsCCEct1szGgQNeQkio,2350
official/core/task_factory.py,sha256=RtQLuVmJT2v0wxQMJuaah69z8n2u7q8N6MxtDKeDJy0,2513
official/core/test_utils.py,sha256=0mLqwX-5aCBxz6gOh7q__jkKqvxFPboUkITxM7liJnU,1802
official/core/train_lib.py,sha256=Du_r400gToRqYH0H-VtaGmYHjqzWyxyX_X2wxoGoptU,5562
official/core/train_lib_test.py,sha256=wh3Huj1J_-X7gX1Gm7sCt4BbTKL_MDmNR5G80zmo8TY,7917
official/core/train_utils.py,sha256=FR6sN5tZbfUzQO0RcKJ4Zv_ys9hQPma-jr6q9wrRT00,17752
official/core/train_utils_test.py,sha256=q0H7hAwjg_YQtQxlZTyDUqwmeXrcTL70iCOm3chfe-I,3495
official/modeling/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/modeling/grad_utils.py,sha256=UkB2SMAa-7Z9qlXDqMfC3uga46n_lBwUIeGqHaQNgqQ,6730
official/modeling/performance.py,sha256=vkZs1bxNwi3zZ0wRmRue9ie6fBB49NAbR6_jEGdjvMY,2242
official/modeling/tf_utils.py,sha256=FT2rIMUZYJ4zK_-C_f6mom6ssc9v61bKnYIo-mMcI_o,6319
official/modeling/activations/__init__.py,sha256=iYSo2x1EJHMfVEVb-OWGGaDtfb9oFZISdriSo-PCmLg,992
official/modeling/activations/gelu.py,sha256=mFZJQggmm2HETE0ZL-OkV5XSfm3wevoa_h31eDhcfzY,1037
official/modeling/activations/gelu_test.py,sha256=yvZMBqdcjOV400UfKOFEU_yylB5hxAgo1CBgVIGLs58,1212
official/modeling/activations/relu.py,sha256=l_xgWBSScOU2t1x4MzzfcXzlv2C2lKQB8iwfEymTNYU,984
official/modeling/activations/relu_test.py,sha256=VxXtgGaOr0eS2QZHmXF0CtBMNaqskTwENB3Q9jvca3o,1200
official/modeling/activations/sigmoid.py,sha256=AW1_c_-TXLenymI7rzVp8IDc7bU4pZQPifhZMTcikZ8,1041
official/modeling/activations/sigmoid_test.py,sha256=1c6pCxHyMtAakOp6py4ZiQFzI2dWDJRLYN4lyOhe540,1364
official/modeling/activations/swish.py,sha256=A66rmyibb2squ4NrjV1_2f_xusql1vFOXZ_QB8Qip0s,2291
official/modeling/activations/swish_test.py,sha256=RyyUbSQblL3F9ZpCrjQoSB1S6KWaXiB8p6PXR5zDqRw,1566
official/modeling/hyperparams/__init__.py,sha256=DjwQM3fhuytANrrFfpEPKsTnJ6Zf3M8-mzdX1lho_k0,846
official/modeling/hyperparams/base_config.py,sha256=OBr2NNjnIVc5Qs70L1YW0UssZmXvTVF94re-J1L6oG8,11213
official/modeling/hyperparams/base_config_test.py,sha256=lZRoGZFIoISDk9UNeXchBPzAc_58yIhwnAbzoqB3Jx0,11413
official/modeling/hyperparams/oneof.py,sha256=slJ2C0gwDVZROoJAfqR5Dc_WEKZyw6UoW_I51wXfC3g,1870
official/modeling/hyperparams/oneof_test.py,sha256=8Kn8GZHupl7JL1fgG15bsV4H8nlo33_UI9uLo6xkTvM,1882
official/modeling/hyperparams/params_dict.py,sha256=yzqD2ybBJDn6X7YFjmao6LmSmrwBHZMAMb6lGvRVKgQ,16402
official/modeling/hyperparams/params_dict_test.py,sha256=4NgJZEZvCYb9wQI6qqOhdWw5-6CArVJ_JLtmzqhQLqE,13657
official/modeling/multitask/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/modeling/multitask/base_model.py,sha256=34TzO6-_3h4TkbaVftPHWbnDINIW7xQ3-xJjPEACiFI,1538
official/modeling/multitask/base_trainer.py,sha256=e0aUPLKVj2p-DHTsFZ1StoQTkDRx6N8JXfH13OGj-yE,5846
official/modeling/multitask/base_trainer_test.py,sha256=tzoGY6KHGxsmXRu_urWzzTJAJs1YO3QIzR8zpF3k9Tk,3653
official/modeling/multitask/configs.py,sha256=dNcVWFzUkeiOwn6mGxbA3ctu--ggF38-aA6KThnxTZg,2531
official/modeling/multitask/evaluator.py,sha256=fQoory04C8mZh57Cj9m3fOGMPHaWMn9KXxwdTaRTpg4,6068
official/modeling/multitask/evaluator_test.py,sha256=bhgKmq9qkUPcclTnORaVMlczkGMyt_k6bMeG0-3Hf2c,4633
official/modeling/multitask/interleaving_trainer.py,sha256=to7-kwRsX1VVGxD76pk2mYd1wWqp_N93Ydg5ec5hkCU,3948
official/modeling/multitask/interleaving_trainer_test.py,sha256=w2Ba5FMnE4OvEXHcpYgom3XPu1VZ4LYWiFxge7aYzeI,4295
official/modeling/multitask/multitask.py,sha256=KY9iBOAArGsE7Mpm7zUFTa2S940mAfWMnuuqBrlAZOw,5714
official/modeling/multitask/task_sampler.py,sha256=Y58ZVjTTSTm0NaKgaUHG_PvWD_P2jX1xWSqpa3RqVx8,4887
official/modeling/multitask/task_sampler_test.py,sha256=WBdkYRMC0F4abUZp6s1ElDsSJOyzpr-uW66j6YZAdQQ,3027
official/modeling/multitask/test_utils.py,sha256=AzcHZdH_gTMahnq-26iq93cIGzwUliUhw0EKtym5dtY,3887
official/modeling/multitask/train_lib.py,sha256=53UbIiqAPvOYo0vgfXuXPtlcsv2lqqiXtsUi0iwZKyU,9883
official/modeling/multitask/train_lib_test.py,sha256=HrY-8DvPa8yx7AhDaGBxtYrWIlBEXbTLk8vRXBaxFmo,4622
official/modeling/optimization/__init__.py,sha256=wmHoi8WP2bzBm9_K5FwfEL5y4t1hCVZpiOrF6-uNzcE,1201
official/modeling/optimization/adafactor_optimizer.py,sha256=pWqti9WPH20IZwJ8E5DgPTQ7_bozlelyHOfAySLhAz8,792
official/modeling/optimization/ema_optimizer.py,sha256=zCTFVGWKHzblSouHMdh6Ry1rwUQOw_oFw83iaS8R9eo,9086
official/modeling/optimization/lars_optimizer.py,sha256=DeMPoF2DEWWZ51s125UM8SIgD47aQOUKwhaIIY5oYgw,7331
official/modeling/optimization/lr_schedule.py,sha256=7bkjsdbQprvc322an3AhYPm7W_iAfwQEUyjC8fNodzM,19106
official/modeling/optimization/lr_schedule_test.py,sha256=L9tVVfS1r4gCajyznnGLeSmMIborvHtoV1WEYG8igpU,3951
official/modeling/optimization/optimizer_factory.py,sha256=fsEJmgShb4CQTuivQAGtBw7Ox94BBvbHGDswmsiLQyU,7636
official/modeling/optimization/optimizer_factory_test.py,sha256=DK55UgF2uignDW1ECqlSGYTEE9niaDDZ0DTvKHqaqo4,14110
official/modeling/optimization/slide_optimizer.py,sha256=bCLUJBqcktqnRPcEz5fiCdV-YfMbvfBtPLdhW2SEe-I,707
official/modeling/optimization/configs/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/modeling/optimization/configs/learning_rate_config.py,sha256=CIaHU77vdRYigWp1Hd6jIE8AJyZCc0Pc__00MN0Ashk,11125
official/modeling/optimization/configs/optimization_config.py,sha256=us0Quu8zweUXd0_yt3BawiZ_KROrxutUFFHrxrGTiKI,4518
official/modeling/optimization/configs/optimization_config_test.py,sha256=Vbxuz09Vb67N24bXR754d08MoljC9CJVTUJikBL5QRk,2009
official/modeling/optimization/configs/optimizer_config.py,sha256=-ZN2d7Vqdc4iMPQzAfaVfDUuoCLeDq6DZFIveyzX7KU,9338
official/nlp/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/nlp/continuous_finetune_lib.py,sha256=xmw5cUORbnCeQZFfY3SQPuynoHYZ8igfVtcdzmW3zOY,7884
official/nlp/continuous_finetune_lib_test.py,sha256=SyW8OaGkF5WnWBd1bzPbUrNfxAuqn-q1ioWyQgP_ukA,3182
official/nlp/optimization.py,sha256=nAdwBfF5GK561B8XIpoq4aV_K547lQf_if3RirBC4Zs,9024
official/nlp/train.py,sha256=jcz_ON4he_YNmGW88oNvHPIvq4H5RCIEa-bd4DZYejQ,3043
official/nlp/albert/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/nlp/albert/configs.py,sha256=77db-5hybA9kpgLGTbhdN3apSs8726nD-QdvbCB3src,2009
official/nlp/albert/run_classifier.py,sha256=E3gS6TaF9E9pLH5RZz8iOqozOXaSmv-5no7cv4ptou8,4065
official/nlp/albert/run_squad.py,sha256=6gMJ-rGObl4cIF6GWH1thQMrySOIyDkIfxd8DlG33L0,4689
official/nlp/bert/__init__.py,sha256=Y1MXbvwHDSF4QLfTyTEgRhuZki6TOh60KIXQIeBa39A,610
official/nlp/bert/bert_models.py,sha256=atmDoASifppwG2osCVAgD1bjfMong77vNch8PBUkSww,14926
official/nlp/bert/bert_models_test.py,sha256=OURZRVN1zeWOTmG-jCnrynTzyWS1Ekms2w-Fa-vpvP8,3877
official/nlp/bert/common_flags.py,sha256=2HmQkE_6edBtsLxcENJL16HidK6nWU8TQy7ZsApJeDQ,4919
official/nlp/bert/configs.py,sha256=DpOVYyNFn-Z5Q_uo5YLhFlQKMvfYJI9A64c_wTambNk,4167
official/nlp/bert/export_tfhub.py,sha256=-dDmlBObMkXG5EinM-qhuN0-V25NZI04TCdPKgMgnK4,5960
official/nlp/bert/export_tfhub_test.py,sha256=scSbX3M6-aP7C_qHFmGwIEEeyvURdDbqonjsdMMbNyY,4682
official/nlp/bert/input_pipeline.py,sha256=1d_yoTU_e0TKDy2Hib0xQ-TAxEP_kB2dyWS_C3IdG-M,11724
official/nlp/bert/model_saving_utils.py,sha256=hTircc2RnBiI5-qFGiBQGPg8IRHUY8FIjRkcPBb-94c,2876
official/nlp/bert/model_training_utils.py,sha256=YmUmPPNmabhMvLdLWWTvQg0-DH6tSqS05ZskQyJza0U,25317
official/nlp/bert/model_training_utils_test.py,sha256=NreCExBD3Lusq5KH3PaKe9riFqneZqnpII8Feg1vBZY,11699
official/nlp/bert/run_classifier.py,sha256=xwB5-niFfIfVUXBtxFX5IHpDEShfaS4WUuPueNIgxnQ,18865
official/nlp/bert/run_pretraining.py,sha256=Yzh9DtUqPXWb9brJn8C1YX1-l0nN9vboidQz_z4FBGI,8455
official/nlp/bert/run_squad.py,sha256=mTl5gqwJ0K6xkL5YR9B7lJq5kF6pFVNas4JdwtL_j2c,5359
official/nlp/bert/run_squad_helper.py,sha256=HvjRvoT-GmweG8S_30zkQt2Cy4IHa_TfCI4lNyjxcBY,18988
official/nlp/bert/serving.py,sha256=bAxEVO6mBWAZ5GZkK8UA2x3K4E1v-2tixZ-6U_KKDxQ,5048
official/nlp/bert/squad_evaluate_v1_1.py,sha256=ih_k9JRdLxLP7E7_EytzrpNUQw3sb6m0uuojzgiDJxE,3724
official/nlp/bert/squad_evaluate_v2_0.py,sha256=GyX39_Tvx2Ek_PrVfGqNXh-rb1Sfi51-e-4DrUdhauU,8625
official/nlp/bert/tf1_checkpoint_converter_lib.py,sha256=js39FKFO4HMA0nDwRClodr2lXHIpfslPC23fruOVroE,7865
official/nlp/bert/tf2_encoder_checkpoint_converter.py,sha256=7O4bQRTXrcpH16vN7zI7lSvRJnG-71h-A6UBTN-ZyXA,5881
official/nlp/bert/tokenization.py,sha256=wg2LJLuy0DTuRJc7hwvUvrVH0MwW5rJRvLRMlfYR0jk,16591
official/nlp/bert/tokenization_test.py,sha256=s34zjDOVLgVEvAKeG0hYZECuUv5mPgYLHgy9JYxmang,5216
official/nlp/configs/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/nlp/configs/bert.py,sha256=eTUjc7aZEmYfzxlEFlgLoi9Syg-ReV0CnJ8Myw6xp88,1423
official/nlp/configs/electra.py,sha256=qCGuxKjqoiASBubR9tIeS6dJqY_8HISiV65aMSjXyAc,1381
official/nlp/configs/encoders.py,sha256=P-jMCNanXFvCll2t6tWFymYuWbLhen1s3Ndf87jnpBQ,23085
official/nlp/configs/encoders_test.py,sha256=j3V63S0dF8aMWTmXfumqFTxB3a3y5Fm_oC_wByKDQug,1527
official/nlp/configs/experiment_configs.py,sha256=1RD52i9I5m8Gg-ZfzJTjTql5pRmlrOHgBmu31bHDT-4,903
official/nlp/configs/finetuning_experiments.py,sha256=_YlmE1OP8TsKtAcToxTmOSQk94veeKjfxuUpNOzRGmE,5064
official/nlp/configs/pretraining_experiments.py,sha256=cZDaA-3-7TkrVmQnuR1SgPUYlHReMVDeNZ7lFK9wfY8,2968
official/nlp/configs/wmt_transformer_experiments.py,sha256=yWwl54CqeUWJwnoVIL4PUfWZ6zmG2OPEs6QKyOjJK70,3810
official/nlp/data/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/nlp/data/classifier_data_lib.py,sha256=SHmnPpM50aVqub3-pwRzbfRkNpfPtAB7aevIx8M6T5o,56380
official/nlp/data/classifier_data_lib_test.py,sha256=jHuzWtf3BpeB049pUpyRcyrVI2I1yxHpTuj1QW0ip7w,3351
official/nlp/data/create_finetuning_data.py,sha256=cZR1hTDQkyA_UZDbOG5eYIg4mIipUWleuccRT30p5xI,16757
official/nlp/data/create_pretraining_data.py,sha256=1WDsSPcqCCuBIQ8HWjxvgYeCcAoRnBw3T0oTR_rzJjU,23359
official/nlp/data/create_pretraining_data_test.py,sha256=DlD0TlOcyKQek8_0pT0OW3GDeZ6ZHv24YVSFMANmUaU,4857
official/nlp/data/create_xlnet_pretraining_data.py,sha256=JrusllIiMfy-_pc_oU0eL9ErIOJ2AynXyNcDqjeIlUs,24191
official/nlp/data/create_xlnet_pretraining_data_test.py,sha256=OPlEEg_doickM9pSHmvHaRv9vbGNKgo1wDhaVJet2qs,10930
official/nlp/data/data_loader.py,sha256=YnLPsStdWMNKMSF0ch0q2FE1DUoi1zvojTC5VWXZNjw,1688
official/nlp/data/data_loader_factory.py,sha256=m4nOiZn0jAzNb65gaH7n2nfpRW4XKTheskO8V9jG9ws,1788
official/nlp/data/data_loader_factory_test.py,sha256=L0qSMiJ3SQ7WQQWTvWajBbib6pgPHm_PqD2DdLTDlm0,1325
official/nlp/data/dual_encoder_dataloader.py,sha256=dYczqh-b_JYTU-bqDHujZ9c_vVLgHkT4bt8v0xZ01-I,5657
official/nlp/data/dual_encoder_dataloader_test.py,sha256=L_-PAGlmJ1eJ-y2KdF0CG0xKFaAhXTMVkENsnEePJ28,5040
official/nlp/data/pretrain_dataloader.py,sha256=ObmNz4hHF_fhJCw8fr6VXkoMPhH-HGWKoK2VOy0NGxY,24634
official/nlp/data/pretrain_dataloader_test.py,sha256=zyPiD3vX7EzUvpHRmDumD_dfAtzT7B6j9uoT2_pF90k,9141
official/nlp/data/pretrain_dynamic_dataloader.py,sha256=yYBVAP8jtyRAXAId3g2qsbtHcb2rmidXmc0Jto2vDQ8,9420
official/nlp/data/pretrain_dynamic_dataloader_test.py,sha256=ukQGOpjX5luhySbj40ouiW-4M74OOKz58CgSNNfmlbs,9620
official/nlp/data/question_answering_dataloader.py,sha256=WPMpo-eU-6wISaM0B_W49kE40l757K8aN40u2kDrQGk,4308
official/nlp/data/question_answering_dataloader_test.py,sha256=0QCdWV29M4jKJtwkhKUh7hkdZZVR6cFvc4wBgF2jKHw,2830
official/nlp/data/sentence_prediction_dataloader.py,sha256=oGTgDSf7pEzuWrQNA3mFESDX1PSCbRPJZLwhXZFxSn8,10337
official/nlp/data/sentence_prediction_dataloader_test.py,sha256=XQH_s06G8jXAD-cskfh_lBLpgcpv5c97mi9FJDL5b-0,11662
official/nlp/data/sentence_retrieval_lib.py,sha256=_Lglpv53Kf2lV5wnP2vvs3Nde5RkYWgCsmVDgzTRvvY,6412
official/nlp/data/squad_lib.py,sha256=iNEFkpwjnjAL_AG-JyXs4lml3nsQXJqPJYKstQJwCkw,36511
official/nlp/data/squad_lib_sp.py,sha256=5lsHLP2hKy0lBJTGuEGhHpcsKSXrooJh2-FfQNZaM_M,34938
official/nlp/data/tagging_data_lib.py,sha256=HH_t8vZby9f_XOI_V-ISCVkgOlzDnOINADDeXh7EK7w,15488
official/nlp/data/tagging_data_lib_test.py,sha256=IH8KTvWxlw-K4GeT9cKJJFqKsXokNYSYKGE3LUAcu08,3736
official/nlp/data/tagging_dataloader.py,sha256=3qVdvJ0g9yZ4O6gDcM4je4oms0iofvniyGUBq4nQrfc,3181
official/nlp/data/tagging_dataloader_test.py,sha256=bYuKKFDnJ1hiGAUk59APII_X9NMyLZqwxI3YHDFEJCc,3196
official/nlp/data/train_sentencepiece.py,sha256=HSXK34vEJQp5NVYLg8ArLxz-IH93zsq1Kb6zMkK_le8,4517
official/nlp/data/wmt_dataloader.py,sha256=wh7fhKp_ZcaRlc_Zk1OLKU9Uwsho-w1Zu-SD3QJcczY,11174
official/nlp/data/wmt_dataloader_test.py,sha256=gNi2Bn9F4sfiNSiUOwM5Ia3kuLo9EU2ijsi90Xl_n-8,4892
official/nlp/metrics/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/nlp/metrics/bleu.py,sha256=7ZrQysixTGL52gyLLM2zi4MJ-XVlsv53vs-KqJtI8bU,6577
official/nlp/metrics/bleu_test.py,sha256=4E8vywwXVGE3QukFJ9YnxqzQbP8xFmzaGTWkpgR9BZk,2498
official/nlp/modeling/__init__.py,sha256=7Qyybe4-7V-341DOx9pgQBcHWDvsi6HtPmeYXSm8ED0,1062
official/nlp/modeling/layers/__init__.py,sha256=6rdmS9Nljh7u0qtrpvvOD4xoSRMzk5GyO7AECKHf4Rs,3562
official/nlp/modeling/layers/attention.py,sha256=xul4RDVcd_Mqw9ioIzAiMv7dTnep1stwhGVcxrW_2pg,3909
official/nlp/modeling/layers/attention_test.py,sha256=SRsYxUONGNSAxbPAAIC_MZbO2W_BJN_8uLHcUoPSO3M,3681
official/nlp/modeling/layers/bigbird_attention.py,sha256=5nYqbMbQb0cyMJQTVwxvx5hFZWmgxt9LWG4z8EoSCXc,21026
official/nlp/modeling/layers/bigbird_attention_test.py,sha256=stwtgiqGJMC7m3abeJ94Aj1MOxrPA8Mv9mvEkdv7B9g,2196
official/nlp/modeling/layers/cls_head.py,sha256=6ZV2cbmyHA5klV5uhKnuWNdFBB5zwh8FRlkuxEQqgSc,13104
official/nlp/modeling/layers/cls_head_test.py,sha256=j1_AxKooQ7fEhgNCcrBtI7fg1vf5XDpIvRXS7IPScvo,7841
official/nlp/modeling/layers/dense_einsum.py,sha256=jpA_saqM9LZalR5j_oRKPoCFFAByTfzYYVLjARTpkJ4,7111
official/nlp/modeling/layers/dense_einsum_test.py,sha256=fOPQ2pPqK3-3II5m3VA9KbzihgOX8NWUqADFQ2wsE2U,5309
official/nlp/modeling/layers/gated_feedforward.py,sha256=pyYsCXZ4TvFd-G2FeNYHc46BbQ0wcHOUCk7PFglhYGI,9058
official/nlp/modeling/layers/gated_feedforward_test.py,sha256=PkemMHrJJYVBn-_kOLwSE4453rDy7dOo0ibK-knHczs,4806
official/nlp/modeling/layers/gaussian_process.py,sha256=GWIKWwu9BqI-DyurPcmqrjHl8PlDSkdE3NrRMcNpSJs,20491
official/nlp/modeling/layers/gaussian_process_test.py,sha256=RtYxwORKNGEkRl_Yf_AdNHysKUL5LJYX50en8iHO0hs,10110
official/nlp/modeling/layers/kernel_attention.py,sha256=6k3UISXH0WSZSgTdxAfHfpEr8w1mmtksyJhdTeSsj7w,15968
official/nlp/modeling/layers/kernel_attention_test.py,sha256=HPb6CR0LeAxZ0xiWl5t2r99IKcR9MoVSeFkRPbzQvPw,4190
official/nlp/modeling/layers/masked_lm.py,sha256=pdqjTBVXT84B8zgZPozaJO6-lDDKzFyKMQ7QNwBz8nI,4791
official/nlp/modeling/layers/masked_lm_test.py,sha256=P7sT8mloLne4LtWvpCe2jXmCQX77UkvwmD-aWN8TFoQ,5853
official/nlp/modeling/layers/masked_softmax.py,sha256=4g-8CnIVX0mf0j0MN3Dtyu4beyZtD3sV8kN-QqDcMD4,3028
official/nlp/modeling/layers/masked_softmax_test.py,sha256=4NMjbkYBv7X5iC0HweTy5TcdyjqcT1ke_Re0zy4jKgM,4694
official/nlp/modeling/layers/mat_mul_with_margin.py,sha256=JY785o14nQGfpuk8YeKQuHHXCthvk19sk6MO-YvFFes,2294
official/nlp/modeling/layers/mat_mul_with_margin_test.py,sha256=cxY6cQFFGxaN5xhwjSipwY9_6p4dUU2kt_zwv-7S0uc,2136
official/nlp/modeling/layers/mobile_bert_layers.py,sha256=Zzlvep41NWQQuhTG6ybqDqSCLwVvNucLRvSE8AYtHYU,22368
official/nlp/modeling/layers/mobile_bert_layers_test.py,sha256=SubM-rYqJw_cN7FI1zqrbNAaQP6gnpDnkrwUHFxp9fY,10880
official/nlp/modeling/layers/multi_channel_attention.py,sha256=8a-6hzyw_NpFPES52H7jYcbujyemBD_dBXQWwK2q_fc,7192
official/nlp/modeling/layers/multi_channel_attention_test.py,sha256=c3iVqihMHBaBC3EnXZfJzvj4olRr6_F3qLjgqodghF8,1912
official/nlp/modeling/layers/on_device_embedding.py,sha256=3brWMi1C6NCFoIUhfsKofcpec2FEdyWgT79Uz7nrGys,3960
official/nlp/modeling/layers/on_device_embedding_test.py,sha256=KWnxZWc16vZNasijxEajGaXmCulhK0zlVohX8r-vud0,8880
official/nlp/modeling/layers/position_embedding.py,sha256=GMaeYjT7JyCjZ7ZWCUo_Vz7ZHGyrFtCPpUoWmyIzK7g,11393
official/nlp/modeling/layers/position_embedding_test.py,sha256=itItqs0bmjYI0Cx8ZZ295qImhyLSNfca4pjTV5yWbPg,8538
official/nlp/modeling/layers/relative_attention.py,sha256=MvHGCfnUOSxkzz0v4aTj71iGys24gKd2NHxje_PEfBw,20486
official/nlp/modeling/layers/relative_attention_test.py,sha256=X1tQ3yo3Gi_2DA5FiVyeLNgnKtX_GIzcrwo-qDqnLzQ,6806
official/nlp/modeling/layers/reuse_attention.py,sha256=_mUHEyhxMilJtSc30HIEnVPps2Al1VWJVBhcJHylyxs,24761
official/nlp/modeling/layers/reuse_attention_test.py,sha256=j3SB68XtC3jQu0au2kY6fZEw26ojJfBNTJbWJsKlUnw,14319
official/nlp/modeling/layers/reuse_transformer.py,sha256=i8_SP0DryWb6BsEc3ZPHThK70Wme3NSMxvDasJ6XnlY,15396
official/nlp/modeling/layers/reuse_transformer_test.py,sha256=rWy7r4QCrN0yR2I-OHWwGtYM285wfOUw2pdaGVV7G-g,17523
official/nlp/modeling/layers/rezero_transformer.py,sha256=sjO5zyoEdA3cts1Ov3IyJECBk4rTo2-CnmoHu-IAsAM,10181
official/nlp/modeling/layers/rezero_transformer_test.py,sha256=Z9w_T_8GUH-iQOJ7IMmPJTRoOgPHcdE_6d6Cq-xE_fo,5080
official/nlp/modeling/layers/self_attention_mask.py,sha256=Jf31Kx_uoHEMfglSQHE_CzjiD8ANZkHHwzpNSBCbnds,2030
official/nlp/modeling/layers/spectral_normalization.py,sha256=_I01P478VucRxWzYUa2pEL5jyD2NCrEe0aTAkOdGtvc,10735
official/nlp/modeling/layers/spectral_normalization_test.py,sha256=djAgaDIz0iwLH_D5T0eICVEzJq4lWBBvfh19ZdGzrLc,3111
official/nlp/modeling/layers/talking_heads_attention.py,sha256=o70GfJBAqPvXkwQallhb0yBq82uYgQKN4ye2bi_dZQI,6807
official/nlp/modeling/layers/talking_heads_attention_test.py,sha256=Jgvz_p19RLS6Q9nYknWaAocwMCEdlygWapccK7wS0wo,7289
official/nlp/modeling/layers/text_layers.py,sha256=Od_AC9d_GtFWToe6fAfAgZxIotyHZbeGxHUjyJvSVJg,26081
official/nlp/modeling/layers/text_layers_test.py,sha256=opG3RmSDjeEGJLfwnvxeKebgQwdx1g02PP9A9qYathk,19148
official/nlp/modeling/layers/tn_expand_condense.py,sha256=JPxV58kzc744j9ofOIMa5GSnBqUwXMWaYt6j1-3cOk8,6617
official/nlp/modeling/layers/tn_expand_condense_test.py,sha256=qW5a9TMlZiVU9d7ClEuJVno9Yw0vL8xWgMqD_nDCbuo,6720
official/nlp/modeling/layers/tn_transformer_expand_condense.py,sha256=Armcw6rliiN2l-I6P6NKjHEXmJfW7uIxk_fkYhWo73Q,11015
official/nlp/modeling/layers/tn_transformer_test.py,sha256=6OkpdVrhtZNUGV8L4qC8OSP9jQ8IMBi-F3ADFj67kgg,9160
official/nlp/modeling/layers/transformer.py,sha256=TviFLALr8A2vdtK8WDUTC7wFWg2boddyJqspUxEAdjg,18312
official/nlp/modeling/layers/transformer_encoder_block.py,sha256=fMusbDgepJdy6PUgAat-IPjtDl6oz5LsMQNBdW6BqlQ,13118
official/nlp/modeling/layers/transformer_encoder_block_test.py,sha256=z4n2uR7hKJEgG2a8VXq9XEJkbicYc7moVaPddeiUMMQ,13129
official/nlp/modeling/layers/transformer_scaffold.py,sha256=jojujQQ_4qbGHIFSBSfmCqNvOb-bV4suYDVtJqCq8zI,13372
official/nlp/modeling/layers/transformer_scaffold_test.py,sha256=k1fxxweIsJevcy6kc3u8xmyyVlpDhDyxpBB-aP7WKVE,20384
official/nlp/modeling/layers/transformer_test.py,sha256=gDRfUyYL-1j2QPpEbF4Ppk6iDuB2eXVlC0cnRfGVInE,3718
official/nlp/modeling/layers/transformer_xl.py,sha256=6NC66wlqOY9t-rBYQgKvlm78Vim7CVF2eDY7ScgGPQA,22109
official/nlp/modeling/layers/transformer_xl_test.py,sha256=s5swNReAek08LobdCUB-O2CXuwn1d2Kx13gRxAZGGbQ,9514
official/nlp/modeling/layers/util.py,sha256=RUbSpmU6FKpu6Fm9Qbi7CdQqKAQXHiqh70jkO3GLShI,1597
official/nlp/modeling/losses/__init__.py,sha256=H-3td2IrVPQm0heoH8eTXhSTZ7jKgYs8JApeC3N1w74,824
official/nlp/modeling/losses/weighted_sparse_categorical_crossentropy.py,sha256=kJvQugF3aAwaZIY_4Axbc27-OnRDpBzQxVm8pEbsWNY,2859
official/nlp/modeling/losses/weighted_sparse_categorical_crossentropy_test.py,sha256=AD690AgpD3gKCOT9g3hYvsUn2NZKlv54l6q3FdxjB08,8619
official/nlp/modeling/models/__init__.py,sha256=wfldMjAJO0KVu9PWJdktGGkOb2DN0QsOOWCIdfBHf3c,1654
official/nlp/modeling/models/bert_classifier.py,sha256=AvQmRMa_-bJ3c2I_zENNWQZ3GN7rPjkRJ_pSG1ozSU0,5896
official/nlp/modeling/models/bert_classifier_test.py,sha256=fWTLMdM9JY6DEZf5YwCzwGc-LiSqERENBgs6kBBPYRY,5092
official/nlp/modeling/models/bert_pretrainer.py,sha256=HsN4dpfbd07DkKJ8NQMEe8sJn2bAhcem1XKjnN3PBYM,11268
official/nlp/modeling/models/bert_pretrainer_test.py,sha256=Ep4j9mQTzxMPgDqoi5LUCLdjUbLMTXmDk8kegpRLTDo,10116
official/nlp/modeling/models/bert_span_labeler.py,sha256=BIwldCi5-SHuZwRS73vXM1QD9NoA-kDRgWSrohKwORY,4997
official/nlp/modeling/models/bert_span_labeler_test.py,sha256=FgYt6o6PKqNpohenfnfTOnIRNPGXUT0Vu0Rd2H_YYDo,5053
official/nlp/modeling/models/bert_token_classifier.py,sha256=ta7xUqkpTlLm3o2pej_aukIsXOs6LXDG275WGL53dB4,5216
official/nlp/modeling/models/bert_token_classifier_test.py,sha256=GTJ6_39VPboYYPt0s9Bfb0npKJys6mxOP5ZxZ-D3M48,5112
official/nlp/modeling/models/dual_encoder.py,sha256=C0Lu3352SbE41GbfLbLMF49Ue4iPUy39KmWuCZLdM_4,6583
official/nlp/modeling/models/dual_encoder_test.py,sha256=KbhKaNlZw-zKgTs_JbObH3pTh14NyEx0qUKsgXlZ4So,5377
official/nlp/modeling/models/electra_pretrainer.py,sha256=Asx3wTAVoYWaijYas0mIKwnWLg7XGxjOT5Ln_mBwCp8,12841
official/nlp/modeling/models/electra_pretrainer_test.py,sha256=v1n1tZd1I_1YqJUyyndCO9uZmUUVTU0JROnjS3-8rLA,6804
official/nlp/modeling/models/seq2seq_transformer.py,sha256=hu-FkBnzIzhI1DX9YnJFb7dwL__YOKNLvl7si1g7q3g,26146
official/nlp/modeling/models/seq2seq_transformer_test.py,sha256=JOM_YSf2oEwZplJYC9It_AxDvgiTKIf47bQALjfjFyc,5402
official/nlp/modeling/models/t5.py,sha256=qHtMFbdnPNxly4_qymhgN4Rz-sqOH4O2pCo6fR8nFJU,50785
official/nlp/modeling/models/t5_test.py,sha256=rLSZBr6cOlZjrPOZcB3sJQl9TKrUsbwC_q4C9w4dSvk,18744
official/nlp/modeling/models/xlnet.py,sha256=Xi_M--3yTdFr9hnqeV0xgGxXAMxCVt2EmcpR-4rzaDg,11779
official/nlp/modeling/models/xlnet_test.py,sha256=k1uOOGb5jj8rEtrjCAhsbLC_sFt05i_ELthyVK4M2aM,13044
official/nlp/modeling/networks/__init__.py,sha256=ck-Pm36BERb80Oeb1U53sgVacp3wqZXUMKRqIvbA6tE,1718
official/nlp/modeling/networks/albert_encoder.py,sha256=nilYxkO8bt-iV0yW5Ea6zFcgOfUpacSRWYuYbaks_sA,8702
official/nlp/modeling/networks/albert_encoder_test.py,sha256=K4ISYRqReBxSKCJG8cjJ93yDi7Vvc68amto5VzA4mPM,7494
official/nlp/modeling/networks/bert_dense_encoder.py,sha256=yxnM3VihHdMnlpXk6sOu7yqEbuLACjXTW6307_SfJcw,11008
official/nlp/modeling/networks/bert_dense_encoder_test.py,sha256=IzXnIpGS6v5700rfj86owwu5zmSXGlfyh6JeSO2S6Vs,14431
official/nlp/modeling/networks/bert_encoder.py,sha256=2UeuM4DoN_eeMmLonWDnKzRBBvHj529Oi9Fmeuntgu0,21836
official/nlp/modeling/networks/bert_encoder_test.py,sha256=6mUApsoLCFCMS5NCmMHhj64Y4bU9zsHghdNIpdf3F3g,24661
official/nlp/modeling/networks/classification.py,sha256=fi1uHCQaMXpOqb1FkDWWEnX2s3ZWlVIrILXc6-kSXNk,4242
official/nlp/modeling/networks/classification_test.py,sha256=ZRDx84NpQ-LmiCZExkEaymOLiPqcIv9suCGD_T8r0LU,7448
official/nlp/modeling/networks/encoder_scaffold.py,sha256=EpZPvqMfLyJJ9QN0t6ARuYWMTHfqaNJBsFw_KpQ7Ke8,16903
official/nlp/modeling/networks/encoder_scaffold_test.py,sha256=0o3WnvvwmMqVEvD4-mwOwqYhWYH8291ItaCeRqagFhM,29410
official/nlp/modeling/networks/funnel_transformer.py,sha256=zfhwue9zHx0m4tONuvhWHLOlzsxTcAPTTvvhY2Xpny4,18170
official/nlp/modeling/networks/funnel_transformer_test.py,sha256=G9BgzmZq4BmIAcQIN_q1j381_QW4wpuXUgoFNKEFrHw,11784
official/nlp/modeling/networks/mobile_bert_encoder.py,sha256=wdtIxqg7bXA59ZFa08prTUgH0vs5WVrWoBB978i2jbo,7569
official/nlp/modeling/networks/mobile_bert_encoder_test.py,sha256=Fd9a1wSNoDgqxCPa1wVZXIJstU97DZDX869PX4eUC3M,7105
official/nlp/modeling/networks/packed_sequence_embedding.py,sha256=m6QUBa_20TcEcTfrUw48jou3GKFQgvlDxFENZd-2G_c,12841
official/nlp/modeling/networks/packed_sequence_embedding_test.py,sha256=mAeeg7YydKkS3xNw0vbvwgX0l4QFWFxJ0eR1rguj-kg,5066
official/nlp/modeling/networks/span_labeling.py,sha256=fMsGyQk_cTOC8bj2PyzhZLcBP0goAIONpP131Zm_4Ds,13033
official/nlp/modeling/networks/span_labeling_test.py,sha256=aEVynl4BTwqXQ3qftUUC7QUKkBguPsSgPDlIBPKvfuU,12330
official/nlp/modeling/networks/xlnet_base.py,sha256=8TUeCZNcWCynYxJksQa2p8PUrMy0JHcoMJ7A1pUgchI,25837
official/nlp/modeling/networks/xlnet_base_test.py,sha256=jwx-_rnLxOsZgA85yglCohr6fgSOVOIjAzxLGdNiYcg,14869
official/nlp/modeling/ops/__init__.py,sha256=jYNm80QiF7ytebnb1P2HIX_AYgF0s0lWHrb3Ri5mdHE,873
official/nlp/modeling/ops/beam_search.py,sha256=dIm_jwXbN6ZCWKT8TuVyxkv3HdrV_86h_Aj658vVRHE,28925
official/nlp/modeling/ops/beam_search_test.py,sha256=bJ4as_yojZjBBzlqiRHOK3cRJlvO7RR8zJWIcJ17TCM,3505
official/nlp/modeling/ops/decoding_module.py,sha256=VyP0nc9vV17ZAI3UZ1TBt1pby0HHgdJvCAj_L9Qi20I,10047
official/nlp/modeling/ops/decoding_module_test.py,sha256=xuVlephsmXevD7XGb2ESI35SZcEuk-gOUrjLGL-7Xg4,2573
official/nlp/modeling/ops/sampling_module.py,sha256=BCYDLCVppqCfEF25NK9wenJdpISyvPzOiJSU1RwUucU,18023
official/nlp/modeling/ops/segment_extractor.py,sha256=6OKXHEeab0ffcEzTHUEBexY1UbT9cEwW3ulDXh-S1QA,9596
official/nlp/modeling/ops/segment_extractor_test.py,sha256=FkyB97iVVJbHDhUIoqdzuRP1v0tAk-N5xROBMgyxKas,5398
official/nlp/projects/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/nlp/projects/bigbird/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/nlp/projects/bigbird/encoder.py,sha256=OHOphR4hPVJxtrkAgtv4QgnAfcf5HT0BYm4w4Ts13ng,9051
official/nlp/projects/bigbird/encoder_test.py,sha256=a7bscpJw3y-bQSEg2bghHjUM3-x7aJNMwDDt2AetqF4,2344
official/nlp/projects/bigbird/experiment_configs.py,sha256=i9kn4fSCc8_snXXMntQPTkvM3Gqn0d7f8o_uImzETdU,3774
official/nlp/projects/bigbird/recompute_grad.py,sha256=0Rmslq0nJwPk5CR7uTCOsvAfb9dmBTDQLcS4rDFlqzM,9027
official/nlp/projects/bigbird/recomputing_dropout.py,sha256=g3T5veYzctZe00Y6-lpV1p5f3O-Pd09PAklIqueKgBo,5950
official/nlp/projects/bigbird/stateless_dropout.py,sha256=tcer6hMUSRJAv7LvJtL7O4a29hso6MFMnY6fEet506w,4921
official/nlp/projects/mobilebert/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/nlp/projects/mobilebert/distillation.py,sha256=rtwKe7MLZOIIjsPsemlUK4tdW1D3SPkDZHaxk3eYQn8,24889
official/nlp/projects/mobilebert/distillation_test.py,sha256=_6OaIgBLMatPGHcbA_WLhj8Dz0YGcHAThfKHDo2Fo0U,7243
official/nlp/projects/mobilebert/export_tfhub.py,sha256=DBX_crKXgXlEecP84gva4PdI9k4BSvk7H7sgE93fLcI,3693
official/nlp/projects/mobilebert/model_utils.py,sha256=-g7GT8hAfL6RuWAKpgLGVcl_lfBPhSPylLKIyOc1Tm8,7470
official/nlp/projects/mobilebert/run_distillation.py,sha256=w86KjmSiBh5NpUQuxbwjizCVBqNiEBQWxt2_Y55IvCo,5490
official/nlp/projects/mobilebert/tf2_model_checkpoint_converter.py,sha256=CEmtzdt_PShUHwoJVNJZPm7FkpgY1wpoipgFS1HBTzU,10713
official/nlp/projects/mobilebert/utils.py,sha256=rqmt71_o3f2xPoHiz-I7cZLhNFDFdNq5vHTM1RHgKDI,1036
official/nlp/projects/teams/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/nlp/projects/teams/teams.py,sha256=JXvSqQGbrVD6p-fVZ3twjI2-lGY0RFWlQKioPGKaWbY,4027
official/nlp/projects/teams/teams_experiments.py,sha256=loI_0XYzhvi7m4ll0ixKkEmT1H1iWjvbxD8ovJCabBw,2512
official/nlp/projects/teams/teams_experiments_test.py,sha256=CK_09PMJrn19jqQpb2sdEaSb_0SoJjf1_buXy62HZrw,1321
official/nlp/projects/teams/teams_pretrainer.py,sha256=krKRbYPKO3MLWpVVWM1w6FfVnFkoTIs7So_-6dssxLE,18516
official/nlp/projects/teams/teams_pretrainer_test.py,sha256=nAHiFTzPPDwTkn8QO3itCyTvVdTo0KnHjGU4T7jzOUc,7741
official/nlp/projects/teams/teams_task.py,sha256=U5mKq68O7bn9MusR4H5PA4B6XwxOCu1BJFn_rQQm0x8,10178
official/nlp/projects/teams/teams_task_test.py,sha256=Q2Hnp47BG2BiqV-pMklz5ZPQ9MPjCQ8aytXrHB3Dm8g,2192
official/nlp/projects/triviaqa/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/nlp/projects/triviaqa/dataset.py,sha256=G9nPV0gzHucb_2hH_HEbIROfXBnN7ZQcAiwIMv2itD8,16324
official/nlp/projects/triviaqa/download_and_prepare.py,sha256=ZSbC2-fWWz9oVnxSEPb5jOd_Sc-ZSPQ11WJRIPmXwKc,2501
official/nlp/projects/triviaqa/evaluate.py,sha256=XFPFaUT9iPIc1zLZfg4BzMoqocgtxnhIMbTutQwxZGg,1554
official/nlp/projects/triviaqa/evaluation.py,sha256=ASJfnwn_IIU6U03fS0duBUvotsU3sFIsnvZkveWsUN4,5293
official/nlp/projects/triviaqa/inputs.py,sha256=-LwtgchqFqhXtpmpeunNAnBpI26ehNyVZ23TFcmSf28,21722
official/nlp/projects/triviaqa/modeling.py,sha256=Fzqq5Cwieiq5WhtjJzXYJv9eBibuS4c27vUWjXFTAu4,4629
official/nlp/projects/triviaqa/predict.py,sha256=KWebwQtlpcU_7Hp9I-KEiramDFgbfl5Q05bhE2QjSqU,7099
official/nlp/projects/triviaqa/prediction.py,sha256=jOJ1yVYjM1uhYgqfNUlr71aw_foHS5AUYSu4XXHFm_A,2735
official/nlp/projects/triviaqa/preprocess.py,sha256=HuA7OgHSGpeLC3NeVDF2A7jZNlDU7ydCRsz6qTaeZgo,18805
official/nlp/projects/triviaqa/sentencepiece_pb2.py,sha256=v-LEdAFEaSxy_RW6OFj_DeT9rYaMJD79IV_2KSGzlMs,10818
official/nlp/projects/triviaqa/train.py,sha256=Hr--PO2Idjct5IZ_NNGmV8U8iABuzHc0nQLvaFtPo80,14303
official/nlp/tasks/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/nlp/tasks/dual_encoder.py,sha256=Jjm0_UGJ004niViUbHUhN4RcKoThNr_a4u51xQX9ZFE,7405
official/nlp/tasks/dual_encoder_test.py,sha256=usjBxqD4luX1iL1gZSwk23V8K02qlaB2wqfTSaMuJHo,4558
official/nlp/tasks/electra_task.py,sha256=uzL1fBa5pxwXOeE0rK8PUoVn57p7FsZ1LG0SmP7OtTk,9637
official/nlp/tasks/electra_task_test.py,sha256=DwdWXTOZPaCoxblBm_TEXpYbpagXje4tBZOORLAyRXo,2282
official/nlp/tasks/masked_lm.py,sha256=YyXAcL7cRk9mFMdNPTuPEfem1O78KCAs8uh-upAYX_k,7703
official/nlp/tasks/masked_lm_test.py,sha256=NDqCGiMGg9iW-CaJrLOZLgZyQ-4RU7B1ZnSQwv5p-tI,2160
official/nlp/tasks/question_answering.py,sha256=cDsv6zErJKWoInadLic0mc8hfxklvPBqL7EcAebRs6M,19814
official/nlp/tasks/question_answering_test.py,sha256=ap2E2RCmHkAjS4IiWoJcPeH5-JS3lRcjMJ_ChByQQ1Q,9939
official/nlp/tasks/sentence_prediction.py,sha256=xPZVnJbgODEPQBpgpcBFbNROc_vYp6czdFUWfDrBhi4,12157
official/nlp/tasks/sentence_prediction_test.py,sha256=9mcE3oUo3g2xV2uHl7Tp1auVpnyi_kzuhmuedul0wmg,10332
official/nlp/tasks/tagging.py,sha256=aMw0jWUCCRvw8syV-SPfbrM6mAE56Hzkb7baS-n7bjY,10088
official/nlp/tasks/tagging_test.py,sha256=Lv7mK0uuxNVsrJSElAuyN8IzV9-CrHoBWSAht8ikliQ,6407
official/nlp/tasks/translation.py,sha256=iVXKehXKbjEn8zQSRl5NrjLJU15GPXZFP3US-z0OQ-o,13381
official/nlp/tasks/translation_test.py,sha256=_16EqVRzHZAApcL23oDEE0ukOR8mmlXVUn5UPSXDyBQ,6871
official/nlp/tasks/utils.py,sha256=P4m3J-qjcZJPXP5MM6nz9J5hdr2VO-fh6Nz4Botre9Y,2575
official/nlp/tools/__init__.py,sha256=Y1MXbvwHDSF4QLfTyTEgRhuZki6TOh60KIXQIeBa39A,610
official/nlp/tools/export_tfhub.py,sha256=4DpIfoZoEkQ-PGhwC25BHAnHziPgxRIXCYkCkBPMcWI,9440
official/nlp/tools/export_tfhub_lib.py,sha256=R_jjZ9uoKe71SbRhs-Pzo58cKNngTIafcFBLNCJ9cXs,21069
official/nlp/tools/export_tfhub_lib_test.py,sha256=KGDc_GWO2a8DqXmgN5kWg5yxj_n9PdLqtBXkGso3lY0,45010
official/nlp/tools/tf2_albert_encoder_checkpoint_converter.py,sha256=t0qv6fySmzVarDWtk0_zOILkUgF_9H9_fEvetEsmv_I,6749
official/nlp/transformer/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/nlp/transformer/attention_layer.py,sha256=fc4IXAZJwvC5GPSw7modTLnhQAn7NeX-s9gBSjR-k0k,6907
official/nlp/transformer/beam_search_v1.py,sha256=gGbo3q8EJqG8C7_F_rMXq57BsWh2fPOByb-ypynLeoU,3679
official/nlp/transformer/compute_bleu.py,sha256=7dZ9SGp_M-99xWhTaOf3q41a3nUSiAMePM2MaRLcyXo,5162
official/nlp/transformer/compute_bleu_test.py,sha256=h8qESpaaHUU619ABggbqBUcR32pJOJ049xiXm0OJ2eQ,2582
official/nlp/transformer/data_download.py,sha256=cRWYIbdD2AQaJYRegWci6UifT9xj9XdQc9L5BIxq5zk,15292
official/nlp/transformer/data_pipeline.py,sha256=8jS0SR4CZ02ItegEvX9A02oNsSIoXi3V-jAqmgH0bbo,13237
official/nlp/transformer/embedding_layer.py,sha256=BzS2fKjBFmG61WL0UKouNc_WJCOQsU0K56ZKpea1h1Y,3680
official/nlp/transformer/ffn_layer.py,sha256=d2r2l7XBGXUauMq_rX3tz2O2KuVStuFEyZGiJw0RFC8,2320
official/nlp/transformer/metrics.py,sha256=izgvgY0We3G-8gUUAElvFCkwSGAFhBY0rlZstOsS-ZE,7007
official/nlp/transformer/misc.py,sha256=PUwBPeVqr9l47D6o4NllYQboBH0JDfmLZGkSTQMeA_A,10343
official/nlp/transformer/model_params.py,sha256=h7mSmaW1vWMrM1XnxmWT7THSb7ItIx8y7ars5faOUEU,2936
official/nlp/transformer/model_utils.py,sha256=PyWTwQcEOQCJ0H8EAQ_a8A2yopH3zxdAl8hD4KzLNgk,4427
official/nlp/transformer/model_utils_test.py,sha256=YF-WGHOUKcWvnUXyfEBpa9GkZ9lLBMBHuhYZOLXTIII,1888
official/nlp/transformer/optimizer.py,sha256=KFWlzVGe3zUHaMrmES2TJ2BbdDN5pJ7MACILU489sBc,2434
official/nlp/transformer/transformer.py,sha256=2TTTPJ6EARuLVF6lL7Eu-ObntJdyw6d3zl0H08ugJHE,21789
official/nlp/transformer/transformer_forward_test.py,sha256=AooWq49-ccxS2FGRUaxCNQJkw8BDgZJKTZJUgSJ2Naw,6052
official/nlp/transformer/transformer_layers_test.py,sha256=zylSR2wGIuZlnnBeF2y_bcwEfTVgGQlcQSBrx9Rk8Pg,3554
official/nlp/transformer/transformer_main.py,sha256=5k6a5nwVJ21uQ4_SM50m5XI_QLaHfMH6wjqNygZJl_8,18237
official/nlp/transformer/transformer_main_test.py,sha256=xK_zDnDsHs-P3on549JD-DVxmcy2zKeWtV6nrCrqsKg,6625
official/nlp/transformer/transformer_test.py,sha256=fP4y858AV-bDWKATMmwsUHFqKbo0Qwaz-1vyO3IiOnQ,3622
official/nlp/transformer/translate.py,sha256=HEpsB2z6AIrKKqD-uWcCIELgJelgqef5SdAnPhEIGzk,6948
official/nlp/transformer/utils/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/nlp/transformer/utils/metrics.py,sha256=CuhU1ZELiHT0ZFciTAJiUxtunUQlg0Z_NmxrDHm1yGo,16706
official/nlp/transformer/utils/tokenizer.py,sha256=MYcGUrAXo78sfgeybhY9PTGmI-r4F5YYYoRxzpAhKBM,24517
official/nlp/transformer/utils/tokenizer_test.py,sha256=6JthsCGXlaqaRV8PMz00taVN1IB12rrMUZaQc4XN0es,6655
official/nlp/xlnet/__init__.py,sha256=Y1MXbvwHDSF4QLfTyTEgRhuZki6TOh60KIXQIeBa39A,610
official/nlp/xlnet/classifier_utils.py,sha256=1-Sr0VWW2GxTxXCmjx_0wCI3ucmHHXTNCP01i9OsS0Y,5418
official/nlp/xlnet/common_flags.py,sha256=QsZN2btNqxYlbIHSSjFFM1fCjMDMp8mkvr12CUCeI3M,5367
official/nlp/xlnet/data_utils.py,sha256=MZGS4gonYqUyrOW5kgPv4jEvy-eug9R52KoX6o7uZKM,30095
official/nlp/xlnet/optimization.py,sha256=GB0ywrvdjMOsbzjT0S9l6SPBN6z_C53413WGQldtB2s,3762
official/nlp/xlnet/preprocess_classification_data.py,sha256=JLx8lHn98PGlaZLfuBc1dOp2crKljtAFblC_ymO-g4I,15338
official/nlp/xlnet/preprocess_pretrain_data.py,sha256=xfDN1l2CQ4tg8Tusce1RMQZCMx6dDGV2QYtJV8XC1JM,32397
official/nlp/xlnet/preprocess_squad_data.py,sha256=5ikk2DxAEkBUripjdw3Vlk_b-oKaR6wvUMc6cuRJQUY,4043
official/nlp/xlnet/preprocess_utils.py,sha256=p__RVGy6vO4HJ6UhCBV_wlnZ_QIDrODg15e04VR9Ia4,3693
official/nlp/xlnet/run_classifier.py,sha256=9JvHRwWHZjKIZiZt4JZKSr8pGkcMMiAIpLuBcbH01oI,6958
official/nlp/xlnet/run_pretrain.py,sha256=w5pqlPTxxQjHJVnfq96HI2JcjsLiG66Vr6Xnt_KSDMk,5692
official/nlp/xlnet/run_squad.py,sha256=U1HAXrhm7h6InvqhQPlPjIGT25zv6mPBBP7ivpvzt9E,11576
official/nlp/xlnet/squad_utils.py,sha256=iChy7H9xAL4BQVzf90a45KkJf1Ev31lt656OrnOpgm4,32269
official/nlp/xlnet/training_utils.py,sha256=04uq7a2ZBGJDPW6KhC7LSTaGkGm08Afx6-jDt7TISi0,11658
official/nlp/xlnet/xlnet_config.py,sha256=jTSRgBtAf4fAAH8xAd8oR-fuyBextSPB5cCes-PveYA,5894
official/nlp/xlnet/xlnet_modeling.py,sha256=MSktp3P__zfXiYESvU_0I_VImIcdNfJiaoC_NLUDC2A,47593
official/recommendation/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/recommendation/constants.py,sha256=F256GplXPbb-2vWENoNPigW-bnVI4Mp5I1Er1-qZsoM,2877
official/recommendation/create_ncf_data.py,sha256=W7XpZLj1pnAmK4p_lnD4ND8kMW1NWZhSboq4fv4hl2o,4008
official/recommendation/data_pipeline.py,sha256=m31TnRlC_-BYE_f6DObrAGJMKNInLXdim1vQez85DXQ,37218
official/recommendation/data_preprocessing.py,sha256=LLJ2dBD24yXNI5No9r8Z_dsNNGT2JahstCMZ8ovuBLI,10357
official/recommendation/data_test.py,sha256=EXlDiRELKo6GUbPcJgaIFRqegQqDmkxBIn1vTxcsq7A,12821
official/recommendation/movielens.py,sha256=rsXCkj3qa07Q4fG9XafoqN35xMgmGG7Q-4S-k23ijJ8,9733
official/recommendation/ncf_common.py,sha256=3btiZkUVaXzYSKIEZq2oroGmP6fvSFFadxK2sBP8_mc,12278
official/recommendation/ncf_input_pipeline.py,sha256=qAUohm0au6BYcJjBfEV7TX_-sX19EtV6DmVA98d3idE,6964
official/recommendation/ncf_keras_main.py,sha256=_9QE9O2Nhkxiu0sJoKlrZwY3emC1KQsZ1_4erqg-SNk,19840
official/recommendation/ncf_test.py,sha256=y583Q1UC9FLua2tGHtM1WrcwNLvaWVs1JB3bNlXRKWQ,4134
official/recommendation/neumf_model.py,sha256=INtFO1VTnwVjubAMmdvkKWF2ubAP5r0N8_ldDBzjrZw,16940
official/recommendation/popen_helper.py,sha256=k87dKuYUVdgo4ixt9_NoD2w2lngs3rmWPT0IVDL1L-c,1917
official/recommendation/stat_utils.py,sha256=jRBrAP_6VygS-u7-lShUQlDGV0EVxDQD9bMx-0tKF_Q,3076
official/recommendation/ranking/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/recommendation/ranking/common.py,sha256=kxU9iY7AxX3cFn9Jn_bTp_c5wIMMy0EUk7c0SlqDiEE,3988
official/recommendation/ranking/task.py,sha256=tbFffxlIyJXODgF-nH3povT1XMJWO8Q-_xtLcImYGsg,7689
official/recommendation/ranking/task_test.py,sha256=FNYqGn8eqf-rnsJy5xUvuFxd5oLFAzeamMJdmHR8kFQ,2182
official/recommendation/ranking/train.py,sha256=xMWygk62PW8w5whx29hS_qYmT4b1tm-L2pNo0wEirEk,6613
official/recommendation/ranking/train_test.py,sha256=KLaUTQ8CeMRfM7CMsPMjYWs5d54uhYZPEGY4SrUU87U,4823
official/recommendation/ranking/configs/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/recommendation/ranking/configs/config.py,sha256=ydI6OYIbRq4Ni9Kfu3K8Am60Lpktvb1kSAWag_GhQfk,10418
official/recommendation/ranking/configs/config_test.py,sha256=EiTNL2WzvitUSewbZE3Eo37cfbmtGpaYOTjXkcBr-S4,1464
official/recommendation/ranking/data/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/recommendation/ranking/data/data_pipeline.py,sha256=uIBBfprkLRV5JnzBtweMNJ5643MytlVJyhPQrKSzRRU,7319
official/recommendation/ranking/data/data_pipeline_test.py,sha256=PAdY5vLY3Ki2_-hx-VbNLANeO6GiIsnpq77o8IHXCHg,2335
official/utils/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/utils/hyperparams_flags.py,sha256=gg6_a5MavFevRXXM2mrLpqkLBnSJVJ3RGmAYGkXKmlA,4607
official/utils/flags/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/utils/flags/_base.py,sha256=zlrFMsSj7JIQNgGdjtaOad3A3km3kMz19V4lWzK7-lY,6395
official/utils/flags/_benchmark.py,sha256=_AAXqVa40GABn0t-XAd4ifLCmZFDXN9uzGgE4LsUKn4,4082
official/utils/flags/_conventions.py,sha256=bo6SurldK2WDvdHGLCACHg8CDuFb4ah9tnNgMEC6vPg,1618
official/utils/flags/_device.py,sha256=BTQZfC_nkf3QPxG_S6ssIgeeTO0uCcrW7-GKRLffFno,2826
official/utils/flags/_distribution.py,sha256=orf7J4vqMfEEaBNiZ76SPV0nVQeJgXsIiiQJXwMMg7Y,1694
official/utils/flags/_misc.py,sha256=g53s7O1iOzA1eOEtjMgzhVbC6Z7R-UTxBRWI2swIp3c,1541
official/utils/flags/_performance.py,sha256=dukq9U1Mg7s9PeaP5gu88FudLzaH0rm_DmxaZBTVCgc,11566
official/utils/flags/core.py,sha256=-3v_BdUhbX7Q4TPnAvfmzF3eAi6syZWFNOa7WcZcRb8,4427
official/utils/flags/flags_test.py,sha256=v7PrXQvzuzvQe07GqJWRQ892pE2Ip3NQR7m6rhW1ddo,5302
official/utils/misc/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/utils/misc/keras_utils.py,sha256=7jMNer1jT3uxE8asVcXzI76wL4vpxyWtwKKku2XoYBU,7783
official/utils/misc/model_helpers.py,sha256=VgQJglcZ23vj0WWwcpOFmzqW0eqmlCPZesWc4cszTAE,3360
official/utils/misc/model_helpers_test.py,sha256=6Kfq9-NJQ9sYcFHNZ6m6fDllEF4lVmkJxlIfEBVCsH0,4549
official/utils/testing/__init__.py,sha256=rHsDzi-79w9d4FQEFWEt4J3p8pvKVZKosw-6yoOnHNk,609
official/utils/testing/integration.py,sha256=4bo3nP3DWGrYcPa20jVt-D3Rc4pir_YyO-ukkst-x24,2220
official/utils/testing/mock_task.py,sha256=qJgN7mTDMVciNHc_0cEV9Cj6Z3w88OWlOhrcpU9oyJ0,3227
orbit/__init__.py,sha256=cFCIzR0s1oAYPUX4bEuu1EH-dzfIc7pBa9MrvGtlORo,1092
orbit/controller.py,sha256=3EAPAzN1o9oDf3fcw31FJiAQ9ArKm6CYh0KzCs49P5s,21772
orbit/controller_test.py,sha256=Ocqybs8T-MsYovfZ-3z6RJ9Hkp2HC-IoqjVX-2chVSo,28650
orbit/runner.py,sha256=f-YGSvkp5ZyUEBkCo8j7xXUVKw1BetoCVUEXMZjHlEk,3509
orbit/standard_runner.py,sha256=mH52s8VVGjYn8UyufNk9MUhf2E5HZKMXCbQsQZ2U0TY,17149
orbit/standard_runner_test.py,sha256=1HOj33Mje8TYCaN0Al01nNi0Rdnh1zM7dQgrWcLG6bI,5100
orbit/actions/__init__.py,sha256=oia7UMsX1jjkkTeE5wffOmntA4RM684yX86c3zr8Evs,3245
orbit/actions/conditional_action.py,sha256=zPEGqiMX6UOE7SoqGzJcdgr6-3vABQ6OT4A-bSXCzLY,1978
orbit/actions/conditional_action_test.py,sha256=YHt5nqGj4G-Bf0tLD1V4ztkRVqYYZu7eYFOkbOcdWK8,1325
orbit/actions/export_saved_model.py,sha256=kBxyCi8qBmpyvLSYIfqViCCkoPcsfD6jqaCst2fec4Y,4990
orbit/actions/export_saved_model_test.py,sha256=IQ3aGk6ZlM3ZllgrqWZGPm9sqt7_H1SpNxTCJ6pLljU,5864
orbit/actions/new_best_metric.py,sha256=l73bVWVtu6B4coDB95AaeX2Krvgtz1UsMBxSr-dmRQU,8479
orbit/actions/new_best_metric_test.py,sha256=kgZxOU85pIJ2PLGOy0rDnG5RV73LKxL0S9tpCinNBHI,4014
orbit/examples/__init__.py,sha256=MOUpI_LL8msomBWW7CpZm-wPJfz_jXOrQidmGVJ0NUU,604
orbit/examples/single_task/__init__.py,sha256=MOUpI_LL8msomBWW7CpZm-wPJfz_jXOrQidmGVJ0NUU,604
orbit/examples/single_task/single_task_evaluator.py,sha256=2CHS_zAh31D4vDCvkxiL4ZCLiF6wLXTk5a0jHpcAjJs,3195
orbit/examples/single_task/single_task_evaluator_test.py,sha256=NnDf-sUalKr7-NgD5BB7bqEPDtwGJoosUlZyaZsLdRA,2088
orbit/examples/single_task/single_task_trainer.py,sha256=qwZMtUcg5gDL-SGkez51fxznAYbXmDxUvqEgCW9vz04,5647
orbit/examples/single_task/single_task_trainer_test.py,sha256=4D8Nwguo6GIHCVinYlfhbTMwo3bwI_qb2zgO_B2CRJw,1961
orbit/utils/__init__.py,sha256=pLvT6z5VTtfinGaHFAtu2446BnQQMWx1NYI8eSTlE0o,1144
orbit/utils/common.py,sha256=WHz0_a0eDX3w-XtEP-pcY9nogbKGsqqS3EYOlCLAnR8,3913
orbit/utils/common_test.py,sha256=g2A_upMZ2YUZhasyvUWMBtFpbqfMv_6HAnRk8W-YyEE,1032
orbit/utils/epoch_helper.py,sha256=9VBx2X_fqamJJZUctqHWu_iUWdDPX_81CmyM53QS608,2136
orbit/utils/loop_fns.py,sha256=PqM2rF-qN1J8ti98jVWmL6z9wqR7Ahj2EOXDkjm9sFs,7915
orbit/utils/summary_manager.py,sha256=Nx8w5HJtIctPF7Kez4lfgarb90L4nZKau8PRRYu2mqs,4177
orbit/utils/tpu_summaries.py,sha256=cGizxMeZphkw1AOC80zwRW7IZKG47ZhG2ERp2vKwJ3w,5631
orbit/utils/tpu_summaries_test.py,sha256=41pMb5y9_UVWJLxcfsyRD80oGaWOp9ZoY9dA0zArrP0,4513
tensorflow_models/__init__.py,sha256=FBG4X_TcENbYM82lIu497jLeZNjGaFluAtZMg0TvvDo,900
tensorflow_models/tensorflow_models_test.py,sha256=kopvnL3XrOA8G9uW1xEFLlnc68cNqidMZrCSSsQElaw,1313
tensorflow_models/nlp/__init__.py,sha256=b2B4NM6vwx3k0dxNmM7UKZ36GjNwFaTiyt1HfEfKIVg,684
tensorflow_models/vision/__init__.py,sha256=60JCW1p4TTWvw_-bqMmTMxRODDW-LXYUIvKFeUzemiw,736
tf_models_nightly-2.5.0.dev20211112.dist-info/METADATA,sha256=dY3Nj5POsd4v2QI6PzuJuOeCoYmTuq-CCS56CXZosNA,1489
tf_models_nightly-2.5.0.dev20211112.dist-info/RECORD,,
tf_models_nightly-2.5.0.dev20211112.dist-info/WHEEL,sha256=gduuPyBvFJQSQ0zdyxF7k0zynDXbIbvg5ZBHoXum5uk,110
tf_models_nightly-2.5.0.dev20211112.dist-info/top_level.txt,sha256=gum2FfO5R4cvjl2-QtP-S1aNmsvIZaFFT6VFzU0f4-g,33
