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Text Classification.json
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{"paragraphs":[{"text":"%md\nText Classification is a basic Natural Lauguage Processing (NLP) task which assigns tags or categories to text according to its content. Text Classification can be used for sentiment analysis, spam detection, topic labeling, etc.\n\nIn this notebook, we will use Analytics Zoo built-in TextClassifier model and TextSet API to classify news categories.","user":"anonymous","dateUpdated":"2019-06-17T20:28:21+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>Text Classification is a basic Natural Lauguage Processing (NLP) task which assigns tags or categories to text according to its content. Text Classification can be used for sentiment analysis, spam detection, topic labeling, etc.</p>\n<p>In this notebook, we will use Analytics Zoo built-in TextClassifier model and TextSet API to classify news categories.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560759799354_-123899252","id":"20190617-162319_164467243","dateCreated":"2019-06-17T16:23:19+0800","dateStarted":"2019-06-17T20:28:22+0800","dateFinished":"2019-06-17T20:28:22+0800","status":"FINISHED","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:32618"},{"text":"%md\nWe first download and extract the [20news](http://qwone.com/~jason/20Newsgroups/) dataset.\n\nWe also download the pre-trained [GloVe](https://nlp.stanford.edu/projects/glove/) word embeddings. Here we choose 'glove.6B.200d.txt', you can also choose other embedding files as well.","user":"anonymous","dateUpdated":"2019-06-17T20:28:22+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>We first download and extract the <a href=\"http://qwone.com/~jason/20Newsgroups/\">20news</a> dataset.</p>\n<p>We also download the pre-trained <a href=\"https://nlp.stanford.edu/projects/glove/\">GloVe</a> word embeddings. Here we choose ‘glove.6B.200d.txt’, you can also choose other embedding files as well.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560760463517_1017234980","id":"20190617-163423_1835563802","dateCreated":"2019-06-17T16:34:23+0800","dateStarted":"2019-06-17T20:28:22+0800","dateFinished":"2019-06-17T20:28:22+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32619"},{"text":"%pyspark\n\nfrom qcloud_cos import CosConfig\nfrom qcloud_cos import CosS3Client\nimport sys\n\nsecret_id = 'AKIDy0ra5NkDUYNwLWqfRxxkKvFBUxF33Tc4' # 替换为用户的 secretId\nsecret_key = 'mDQgtyuaz9tLn21kDWcWGwPfiDakmZ5R' # 替换为用户的 secretKey\nregion = 'ap-shanghai' # 替换为用户的 Region\ntoken = None # 使用临时密钥需要传入 Token，默认为空，可不填\nscheme = 'https' # 指定使用 http/https 协议来访问 COS，默认为 https，可不填\nconfig = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token, Scheme=scheme)\n# 2. 获取客户端对象\nclient = CosS3Client(config)","user":"anonymous","dateUpdated":"2019-06-17T20:28:22+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560759778766_665237401","id":"20190605-150954_12919675","dateCreated":"2019-06-17T16:22:58+0800","dateStarted":"2019-06-17T20:28:22+0800","dateFinished":"2019-06-17T20:28:22+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32620"},{"text":"%pyspark\nresponse = client.get_object(\n Bucket='demo-sh-1258997244',\n Key='/20news-18828.tar.gz'\n)\nprint(type(response['Body']))\nresponse['Body'].get_stream_to_file('/tmp/20news-18828.tar.gz')","user":"anonymous","dateUpdated":"2019-06-17T20:28:22+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"<class 'qcloud_cos.streambody.StreamBody'>\n"}]},"apps":[],"jobName":"paragraph_1560759778766_-2076867196","id":"20190605-151029_1584104958","dateCreated":"2019-06-17T16:22:58+0800","dateStarted":"2019-06-17T20:28:22+0800","dateFinished":"2019-06-17T20:28:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32621"},{"text":"%pyspark\nimport tarfile\ntar = tarfile.open(\"/tmp/20news-18828.tar.gz\", \"r:gz\")\ntar.extractall(\"/tmp\")\ntar.close()","user":"anonymous","dateUpdated":"2019-06-17T20:28:23+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560759778766_5894169","id":"20190605-151154_2101628495","dateCreated":"2019-06-17T16:22:58+0800","dateStarted":"2019-06-17T20:28:24+0800","dateFinished":"2019-06-17T20:28:27+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32622"},{"text":"%pyspark\nresponse = client.get_object(\n Bucket='demo-sh-1258997244',\n Key='/glove.6B/glove.6B.200d.txt'\n)\nresponse['Body'].get_stream_to_file('/tmp/glove.6B.200d.txt')","user":"anonymous","dateUpdated":"2019-06-17T20:28:27+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560759778766_-144212946","id":"20190605-153420_95826407","dateCreated":"2019-06-17T16:22:58+0800","dateStarted":"2019-06-17T20:28:28+0800","dateFinished":"2019-06-17T20:29:20+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32623"},{"text":"%md\n## Initialization\nImport necessary libraries and initialize the SparkContext.","user":"anonymous","dateUpdated":"2019-06-17T20:29:20+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h2>Initialization</h2>\n<p>Import necessary libraries and initialize the SparkContext.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560760638969_-759953150","id":"20190617-163718_1731639832","dateCreated":"2019-06-17T16:37:18+0800","dateStarted":"2019-06-17T20:29:21+0800","dateFinished":"2019-06-17T20:29:21+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32624"},{"text":"%pyspark\nimport datetime as dt\n\nfrom bigdl.optim.optimizer import Adagrad\nfrom zoo.common.nncontext import init_nncontext\nfrom zoo.feature.text import TextSet\nfrom zoo.models.textclassification import TextClassifier\n\nsc = init_nncontext(\"Text Classification Example\")","user":"anonymous","dateUpdated":"2019-06-17T20:29:21+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560759778767_1112868371","id":"20190605-153556_1908180788","dateCreated":"2019-06-17T16:22:58+0800","dateStarted":"2019-06-17T20:29:21+0800","dateFinished":"2019-06-17T20:29:21+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32625"},{"text":"%md\n## Data Preparation and Preprocessing\n\nWe first take a look at the unzipped 20news dataset.","user":"anonymous","dateUpdated":"2019-06-17T20:29:22+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h2>Data Preparation and Preprocessing</h2>\n<p>We first take a look at the unzipped 20news dataset.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560760667802_1960128772","id":"20190617-163747_483866871","dateCreated":"2019-06-17T16:37:47+0800","dateStarted":"2019-06-17T20:29:22+0800","dateFinished":"2019-06-17T20:29:22+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32626"},{"text":"%pyspark\nos.listdir(\"/tmp/20news-18828\")","user":"anonymous","dateUpdated":"2019-06-17T20:29:22+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"['talk.politics.mideast', 'misc.forsale', 'sci.med', 'soc.religion.christian', 'rec.sport.baseball', 'talk.politics.guns', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'comp.sys.ibm.pc.hardware', 'comp.windows.x', 'sci.space', 'rec.motorcycles', 'rec.autos', 'comp.sys.mac.hardware', 'talk.politics.misc', 'alt.atheism', 'comp.os.ms-windows.misc', 'comp.graphics', 'talk.religion.misc']\n"}]},"apps":[],"jobName":"paragraph_1560759778766_-1671346086","id":"20190605-153409_721737330","dateCreated":"2019-06-17T16:22:58+0800","dateStarted":"2019-06-17T20:29:22+0800","dateFinished":"2019-06-17T20:29:22+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32627"},{"text":"%md\nRead the texts of each news category as a TextSet.","user":"anonymous","dateUpdated":"2019-06-17T20:29:23+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>Read the texts of each news category as a TextSet.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560760706549_-1983210509","id":"20190617-163826_769717358","dateCreated":"2019-06-17T16:38:26+0800","dateStarted":"2019-06-17T20:29:23+0800","dateFinished":"2019-06-17T20:29:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32628"},{"text":"%pyspark\ntext_set = TextSet.read(path=\"/tmp/20news-18828\").to_distributed(sc)","user":"anonymous","dateUpdated":"2019-06-17T20:29:23+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560759778767_324170733","id":"20190605-153945_988672237","dateCreated":"2019-06-17T16:22:58+0800","dateStarted":"2019-06-17T20:29:23+0800","dateFinished":"2019-06-17T20:29:24+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32629"},{"text":"%md\nYou can easily call transformation methods of a TextSet one by one to build the text transformation pipeline and do text preprocessing in a distributed fashion. ","user":"anonymous","dateUpdated":"2019-06-17T20:29:24+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>You can easily call transformation methods of a TextSet one by one to build the text transformation pipeline and do text preprocessing in a distributed fashion.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560760903441_-1746352575","id":"20190617-164143_1117200549","dateCreated":"2019-06-17T16:41:43+0800","dateStarted":"2019-06-17T20:29:24+0800","dateFinished":"2019-06-17T20:29:24+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32630"},{"text":"%pyspark\ntransformed = text_set.tokenize().normalize()\\\n .word2idx(remove_topN=10, max_words_num=5000)\\\n .shape_sequence(len=500).generate_sample()\nword_index = transformed.get_word_index()","user":"anonymous","dateUpdated":"2019-06-17T20:29:24+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://10.0.96.85:42034/jobs","http://10.0.96.85:42034/jobs"],"interpreterSettingId":"spark"}},"apps":[],"jobName":"paragraph_1560760857590_-603614585","id":"20190617-164057_734634004","dateCreated":"2019-06-17T16:40:57+0800","dateStarted":"2019-06-17T20:29:25+0800","dateFinished":"2019-06-17T20:29:28+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32631"},{"text":"%md\nWe split the preprocessed TextSet into training and validation set.","user":"anonymous","dateUpdated":"2019-06-17T20:29:28+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>We split the preprocessed TextSet into training and validation set.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560761329343_986235371","id":"20190617-164849_1262441781","dateCreated":"2019-06-17T16:48:49+0800","dateStarted":"2019-06-17T20:29:29+0800","dateFinished":"2019-06-17T20:29:29+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32632"},{"text":"%pyspark\ntrain_set, val_set = transformed.random_split([0.8, 0.2])","user":"anonymous","dateUpdated":"2019-06-17T20:29:29+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"editorMode":"ace/mode/python"},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560760872961_-1514734071","id":"20190617-164112_143909841","dateCreated":"2019-06-17T16:41:12+0800","dateStarted":"2019-06-17T20:29:29+0800","dateFinished":"2019-06-17T20:29:29+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32633"},{"text":"%md\n## Model Definition and Training\n\nWe use built-in TextClassifier model in Analytics Zoo with CNN as encoder. You can try to use LSTM and GRU as well.","user":"anonymous","dateUpdated":"2019-06-17T20:29:29+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h2>Model Definition and Training</h2>\n<p>We use built-in TextClassifier model in Analytics Zoo with CNN as encoder. You can try to use LSTM and GRU as well.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560761358044_698945099","id":"20190617-164918_879562778","dateCreated":"2019-06-17T16:49:18+0800","dateStarted":"2019-06-17T20:29:29+0800","dateFinished":"2019-06-17T20:29:29+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32634"},{"text":"%pyspark\nmodel = TextClassifier(20, '/tmp/glove.6B.200d.txt', word_index, 500, encoder=\"cnn\", encoder_output_dim=256)","user":"anonymous","dateUpdated":"2019-06-17T20:29:29+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"creating: createZooKerasWordEmbedding\ncreating: createZooKerasSequential\ncreating: createZooKerasConvolution1D\ncreating: createZooKerasGlobalMaxPooling1D\ncreating: createZooKerasDense\ncreating: createZooKerasDropout\ncreating: createZooKerasActivation\ncreating: createZooKerasDense\ncreating: createZooTextClassifier\n"}]},"apps":[],"jobName":"paragraph_1560759778767_-170059241","id":"20190605-154145_789198012","dateCreated":"2019-06-17T16:22:58+0800","dateStarted":"2019-06-17T20:29:29+0800","dateFinished":"2019-06-17T20:29:36+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32635"},{"text":"%md\nAfter creating the model, we use Keras-Style API to train the model in a distributed fashion.\n\nThe training may take several minutes.","user":"anonymous","dateUpdated":"2019-06-17T20:29:36+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>After creating the model, we use Keras-Style API to train the model in a distributed fashion.</p>\n<p>The training may take several minutes.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560771523657_554611592","id":"20190617-193843_333367373","dateCreated":"2019-06-17T19:38:43+0800","dateStarted":"2019-06-17T20:29:36+0800","dateFinished":"2019-06-17T20:29:36+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32636"},{"text":"%pyspark\nmodel.compile(optimizer=Adagrad(learningrate=0.01, learningrate_decay=0.001),\n loss=\"sparse_categorical_crossentropy\",\n metrics=['accuracy'])\napp_name = 'textclassification-' + dt.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\nmodel.set_tensorboard('/tmp/analytics_zoo_logs', app_name)\nmodel.fit(train_set, batch_size=128, nb_epoch=5, validation_data=val_set)","user":"anonymous","dateUpdated":"2019-06-17T20:29:36+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"creating: createAdagrad\ncreating: createZooKerasSparseCategoricalCrossEntropy\ncreating: createZooKerasSparseCategoricalAccuracy\n"}]},"runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web 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Model Evaluation and Inference\n\nWe use the validation dataset to evaluate the trained model. You can observe around 85% accuracy after 20 epochs of training.","user":"anonymous","dateUpdated":"2019-06-17T20:36:21+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h2>Model Evaluation and Inference</h2>\n<p>We use the validation dataset to evaluate the trained model. You can observe around 85% accuracy after 20 epochs of training.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560771601636_303836809","id":"20190617-194001_483458986","dateCreated":"2019-06-17T19:40:01+0800","dateStarted":"2019-06-17T20:36:22+0800","dateFinished":"2019-06-17T20:36:22+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32638"},{"text":"%pyspark\nresults = model.evaluate(val_set, batch_size=128)\nfor result in results:\n print(result)","user":"anonymous","dateUpdated":"2019-06-17T20:38:54+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://10.0.96.85:42034/jobs"],"interpreterSettingId":"spark"}},"apps":[],"jobName":"paragraph_1560759778767_1306241587","id":"20190605-154656_1558598590","dateCreated":"2019-06-17T16:22:58+0800","dateStarted":"2019-06-17T20:38:54+0800","dateFinished":"2019-06-17T20:39:04+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32639","results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"Evaluated result: 0.5921568870544434, total_num: 121, method: Loss\nEvaluated result: 0.8191207647323608, total_num: 3776, method: Top1Accuracy\n"}]}},{"text":"%md\nFinally print out some prediction results to have a look.","user":"anonymous","dateUpdated":"2019-06-17T20:39:11+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>Finally print out some prediction results to have a look.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560771678453_212957825","id":"20190617-194118_1022957443","dateCreated":"2019-06-17T19:41:18+0800","dateStarted":"2019-06-17T20:39:11+0800","dateFinished":"2019-06-17T20:39:11+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32640"},{"text":"%pyspark\npredict_set = model.predict(val_set)\npredicts = predict_set.get_predicts().take(3)\nfor predict in predicts:\n print(predict)","user":"anonymous","dateUpdated":"2019-06-17T20:39:13+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"[array([6.3005817e-01, 1.8661401e-04, 6.7538176e-06, 2.9699799e-05,\n 1.2928069e-05, 3.8779643e-05, 1.3552454e-05, 2.9828669e-05,\n 9.8125267e-05, 1.1108572e-05, 2.4646808e-05, 1.6886082e-05,\n 3.2444026e-05, 5.4214580e-04, 1.3317815e-04, 1.3522801e-01,\n 1.6860603e-04, 2.7933794e-03, 6.4288611e-03, 2.2414632e-01],\n dtype=float32)]\n[array([3.1693643e-01, 2.4451938e-04, 5.0988706e-06, 3.0225990e-05,\n 1.8084110e-04, 9.4990966e-05, 4.4052958e-06, 9.4156778e-05,\n 1.2212456e-04, 1.8677096e-05, 2.9819566e-05, 4.8370028e-04,\n 9.0474765e-05, 3.1727692e-03, 1.9519731e-04, 5.8279967e-01,\n 1.3775271e-04, 4.6561277e-03, 2.0693573e-03, 8.8633537e-02],\n dtype=float32)]\n[array([3.6989099e-01, 7.4889671e-05, 2.0040291e-06, 1.2715718e-05,\n 3.8195878e-05, 8.2065344e-06, 1.0118434e-06, 8.8948318e-06,\n 1.9896999e-05, 1.7170993e-05, 2.5327213e-06, 5.1679490e-06,\n 5.1830571e-06, 3.3454620e-04, 6.0049308e-05, 5.0416982e-01,\n 2.2341736e-05, 1.3865839e-04, 8.7403059e-05, 1.2510030e-01],\n dtype=float32)]\n"}]},"runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://10.0.96.85:42034/jobs"],"interpreterSettingId":"spark"}},"apps":[],"jobName":"paragraph_1560759778767_873460263","id":"20190605-155406_250292081","dateCreated":"2019-06-17T16:22:58+0800","dateStarted":"2019-06-17T20:39:14+0800","dateFinished":"2019-06-17T20:39:19+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32641"},{"text":"%md\nIn conclusion, this notebook guides you through the steps to build a text classification model using Analytics Zoo. The whole process should be straightforward and the code is neat.\n\nBesides text classification, many more advanced NLP models are supported in Analytics Zoo, including text matching, sequence to sequence, transformer and BERT. If you are interested in these, feel free to find the corresponding documentations on our website: https://analytics-zoo.github.io/","user":"anonymous","dateUpdated":"2019-06-17T20:39:22+0800","config":{"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"results":{},"enabled":true,"editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>In conclusion, this notebook guides you through the steps to build a text classification model using Analytics Zoo. The whole process should be straightforward and the code is neat.</p>\n<p>Besides text classification, many more advanced NLP models are supported in Analytics Zoo, including text matching, sequence to sequence, transformer and BERT. If you are interested in these, feel free to find the corresponding documentations on our website: <a href=\"https://analytics-zoo.github.io/\">https://analytics-zoo.github.io/</a></p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560759778767_-616947569","id":"20190605-160753_1383156291","dateCreated":"2019-06-17T16:22:58+0800","dateStarted":"2019-06-17T20:39:22+0800","dateFinished":"2019-06-17T20:39:22+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32642"},{"text":"%md\n","user":"anonymous","dateUpdated":"2019-06-17T19:55:04+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown"},"settings":{"params":{},"forms":{}},"apps":[],"jobName":"paragraph_1560771854961_-593829613","id":"20190617-194414_1000478256","dateCreated":"2019-06-17T19:44:14+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:32643"}],"name":"Text Classification","id":"2EE2AYYGC","noteParams":{},"noteForms":{},"angularObjects":{"md:shared_process":[],"spark:shared_process":[]},"config":{"isZeppelinNotebookCronEnable":false,"looknfeel":"default","personalizedMode":"false"},"info":{}}