{"push_id":3491531275,"size":14,"distinct_size":14,"ref":"refs/heads/export-D14755727","head":"adc1b447bf4397f2196fa8859c2f98886595dceb","before":"ab4084e0c1f4538ad3a7a209339f85573d856e5d","commits":[{"sha":"944cc11dd46b1e6edfd32c6d1fa745d496fb3cff","author":{"name":"Ruty Rinott","email":"d596de86de11da6b362f0e22e2a9ae14a0fe8fcf@fb.com"},"message":"add XLM sentence encoder (#437)\n\nSummary:\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/437\n\nAdd model to produce XLM sentence embeddings.\n1. refactor XLMSentenceEncoder to extent BertSentenceEncoderBase to re-use pooling Logic\n2. Add a model that only has the encoding layer to produce sentence encoding\n\nReviewed By: kartikayk\n\nDifferential Revision: D14676261\n\nfbshipit-source-id: 004ef4c808e44db0289e8cff72eeaf6e412f943a","distinct":true,"url":"https://api.github.com/repos/Titousensei/pytext-1/commits/944cc11dd46b1e6edfd32c6d1fa745d496fb3cff"},{"sha":"4e15323d30ab7ecf740c003af6c6fff7a119d6e8","author":{"name":"Nisarg Shah","email":"58a81e8aca44d04040d741e9d18069f1140330c9@gmail.com"},"message":"Adding a docnn config for dense features as an example (#415)\n\nSummary:\nThis will help show users how to use dense features. It will also enable us to quickly experiment with supporting and adding dense features to newer models.\n\ntrained, tested a model with this feature.\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/415\n\nReviewed By: hikushalhere\n\nDifferential Revision: D14533972\n\nPulled By: snisarg\n\nfbshipit-source-id: e89aa2c1c9e7c2391d3e18532524e4114ddf8a0a","distinct":true,"url":"https://api.github.com/repos/Titousensei/pytext-1/commits/4e15323d30ab7ecf740c003af6c6fff7a119d6e8"},{"sha":"4e7a3b1b668e9f5eb693fec694c36f8725e7627b","author":{"name":"Ruty Rinott","email":"d596de86de11da6b362f0e22e2a9ae14a0fe8fcf@fb.com"},"message":"ignore utf-8 encoding issues (#446)\n\nSummary:\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/446\n\nreplicating behaviour of data_handler\nfor opening csv files using:  (encoding=\"utf-8\", errors=\"replace\")\n\nReviewed By: jingfeidu\n\nDifferential Revision: D14764078\n\nfbshipit-source-id: aa0531e84b4ae49b8fcb84bbbe7cdbdabe0d6dde","distinct":true,"url":"https://api.github.com/repos/Titousensei/pytext-1/commits/4e7a3b1b668e9f5eb693fec694c36f8725e7627b"},{"sha":"d6054c1d2fa198acda8a199ceeacbd4e673144ff","author":{"name":"Chenyang Yu","email":"fda300022a26d759503391347ae8b7bf6428607f@instagram.com"},"message":"cache lm packed data (#447)\n\nSummary:\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/447\n\ncache lm packed data\nFor every single GPU node, we cache the packed numberized LM data in Json format (pickle is slower in theory)\nThe first epoch, we will generate the numberized_rows and write to cache\nFor the following epoch, we will read from cache directly.\n\nReviewed By: borguz\n\nDifferential Revision: D14651814\n\nfbshipit-source-id: 05b0880e6d0cf979e3cc5bcdf0ea60f3d2702320","distinct":true,"url":"https://api.github.com/repos/Titousensei/pytext-1/commits/d6054c1d2fa198acda8a199ceeacbd4e673144ff"},{"sha":"f501bf9c3942f453fcd2c39e64a1378bbe8c65b0","author":{"name":"Barlas Oguz","email":"ba48291c2a0181745243f23f5d138454a85c377e@fb.com"},"message":"fix - test flow only works with TSVDataSource (#448)\n\nSummary:\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/448\n\nThis diff D14526577 enabled passing test file for new task, but works only with TSVDataSource.  Generalize it so it works for other data sources.\n\nReviewed By: hikushalhere\n\nDifferential Revision: D14781441\n\nfbshipit-source-id: a88b5e0a8d46704450263554a100e7108ca54d31","distinct":true,"url":"https://api.github.com/repos/Titousensei/pytext-1/commits/f501bf9c3942f453fcd2c39e64a1378bbe8c65b0"},{"sha":"6abb019407492f6dfad7650471016b385307e407","author":{"name":"Shicong Zhao","email":"61a4650084e5015154dbf694ee65e8668e7ca60f@fb.com"},"message":"Fix FloatListTensorizer (#450)\n\nSummary:\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/450\n\n1 make FloatListTensorizer work with comma/space as separater\n2 remove duplicated FloatVectorTensorizer\n\nReviewed By: gardenia22\n\nDifferential Revision: D14791878\n\nfbshipit-source-id: 4396a71c8eb2d2494774fbbd23c94e947dc072c7","distinct":true,"url":"https://api.github.com/repos/Titousensei/pytext-1/commits/6abb019407492f6dfad7650471016b385307e407"},{"sha":"2704c1c7a554a3808c6fe6bfeca5067e990ceeed","author":{"name":"Derek Liu","email":"1f5f3ef03fe49a3c70f5455eb9cfbdcb7c8df859@fb.com"},"message":"Add dense feature support (#449)\n\nSummary:\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/449\n\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/444\n\nAdd dense features to decoder in Contextual Intent Slot model.\n\nReviewed By: gardenia22\n\nDifferential Revision: D14754861\n\nfbshipit-source-id: de7e8c946d533bf9d96a11428772646dbac45a4e","distinct":true,"url":"https://api.github.com/repos/Titousensei/pytext-1/commits/2704c1c7a554a3808c6fe6bfeca5067e990ceeed"},{"sha":"3c7ecd48860b9f02680f297873a222fa47d1efe6","author":{"name":"Eric Gaudet","email":"938635fd90d4412d1954b51a78139221070087b9@fb.com"},"message":"inherit RootDataSource.register_type (#440)\n\nSummary:\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/440\n\nRootDataSource registered types are automatically inherited by the\nsubclasses and don't need to re-register common types like str.\n\nReviewed By: borguz\n\nDifferential Revision: D14739237\n\nfbshipit-source-id: ecaf1851b93984c1898e82a95b4bea70250eb119","distinct":true,"url":"https://api.github.com/repos/Titousensei/pytext-1/commits/3c7ecd48860b9f02680f297873a222fa47d1efe6"},{"sha":"fb58d207407d3d38587d18ace67d4dcd8be3c652","author":{"name":"Kshitiz Malik","email":"de54fa6ff135d79296efa72497858d5c8c990bf9@fb.com"},"message":"Fix loss reporting (#453)\n\nSummary:\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/453\n\nWhen computing average loss for train/eval/test, loss across different batches should be weighted with batch size.\n\nThis doesn't matter for standard training where most batches are the full batch size except the last. However, when training in a massively distributed fashion (eg: for Federated Learning), data is split into small sizes. Many batches are not 'complete' batches, so weighting by batch size is important.\n\nReviewed By: gardenia22\n\nDifferential Revision: D14791247\n\nfbshipit-source-id: 57b1142638755868a208dad233070a78537c7f3e","distinct":true,"url":"https://api.github.com/repos/Titousensei/pytext-1/commits/fb58d207407d3d38587d18ace67d4dcd8be3c652"},{"sha":"5f8250c349b13d942e60134e70a00e899c58be8b","author":{"name":"Barlas Oguz","email":"ba48291c2a0181745243f23f5d138454a85c377e@fb.com"},"message":"tie weights in masked lm (#455)\n\nSummary:\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/455\n\nTie word embedding weights with output layer weights\n\nReviewed By: kartikayk\n\nDifferential Revision: D14686543\n\nfbshipit-source-id: fc15f756b1043b6ab7b208b43442c5c13ef402ea","distinct":true,"url":"https://api.github.com/repos/Titousensei/pytext-1/commits/5f8250c349b13d942e60134e70a00e899c58be8b"},{"sha":"3dfc728c8a7b755c03ae46a09a0558016ddb39b8","author":{"name":"Chenyang Yu","email":"fda300022a26d759503391347ae8b7bf6428607f@instagram.com"},"message":"Back out \"[PyText] cache lm packed data\" (#454)\n\nSummary:\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/454\n\nOriginal commit changeset: 05b0880e6d0c\n\nReviewed By: borguz\n\nDifferential Revision: D14807451\n\nfbshipit-source-id: dafbc76b4660eeb296f76eff9ce14b94528d6390","distinct":true,"url":"https://api.github.com/repos/Titousensei/pytext-1/commits/3dfc728c8a7b755c03ae46a09a0558016ddb39b8"},{"sha":"cf99300aa77b4abe26f8a34a3b620f7409a75f15","author":{"name":"Eric Gaudet","email":"938635fd90d4412d1954b51a78139221070087b9@fb.com"},"message":"config adapter declaration (#442)\n\nSummary:\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/442\n\nconfig adapter declaration more robust and simpler. It helps make\nsure all adapters are taken into account and we don't skip or override one by\nmistake.\n\nReviewed By: m3rlin45\n\nDifferential Revision: D14754761\n\nfbshipit-source-id: 0088a977d80cd66ca1a80536a5a8184866974e53","distinct":true,"url":"https://api.github.com/repos/Titousensei/pytext-1/commits/cf99300aa77b4abe26f8a34a3b620f7409a75f15"},{"sha":"21f2212ae68285f0a95d6b8b74b70958ba6dbe33","author":{"name":"Barlas Oguz","email":"ba48291c2a0181745243f23f5d138454a85c377e@fb.com"},"message":"new multitask data (#439)\n\nSummary:\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/439\n\nData class to sample batches from a dict of Data objects.  Sampling is done by a BatchSampler class, of which we provide 2 implementations:\n\nRoundRobinBatchSampler, does round robin on each data objects batches, taking turns.  Datasets with less batches will see more repetitions, effectively being upsampled.\n\nEvalDataSampler, yields all batches in each Data object in turn.  Every batch will be output once per epoch.  Intended for use in evaluation.\n\nReviewed By: kartikayk\n\nDifferential Revision: D14737476\n\nfbshipit-source-id: e934e529754e67bc75d5b52aa2a8e445a78e525f","distinct":true,"url":"https://api.github.com/repos/Titousensei/pytext-1/commits/21f2212ae68285f0a95d6b8b74b70958ba6dbe33"},{"sha":"adc1b447bf4397f2196fa8859c2f98886595dceb","author":{"name":"Eric Gaudet","email":"938635fd90d4412d1954b51a78139221070087b9@fb.com"},"message":"WordTaggingTask migration (#443)\n\nSummary:\nPull Request resolved: https://github.com/facebookresearch/pytext/pull/443\n\nmigrating word tagging to NewTask.\n\nReviewed By: chenyangyu1988\n\nDifferential Revision: D14755727\n\nfbshipit-source-id: 598cd6664d17222609a070d5083c5d042d14f1c9","distinct":true,"url":"https://api.github.com/repos/Titousensei/pytext-1/commits/adc1b447bf4397f2196fa8859c2f98886595dceb"}]}
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