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| \n \n | \n ![](\"https://www.tensorflow.org/images/tf_logo_32px.png\"){kind=logo} View on TensorFlow.org\n | \n \n ![](\"https://www.tensorflow.org/images/colab_logo_32px.png\"){kind=logo} Run in Google Colab\n | \n \n ![](\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\") View source on GitHub\n | \n \n ![](\"https://www.tensorflow.org/images/download_logo_32px.png\"){kind=logo} Download notebook\n | \n
100MB w/o quantization |\n| BERT-Base | 'bert_classifier' | Standard BERT model that is widely used in NLP tasks. | 300MB |\nIn the quick start, we have used the average word embedding model. Let's switch to MobileBERT to train a model with higher accuracy.\nEnd of explanation\ntrain_data = DataLoader.from_csv(\n filename='train.csv',\n text_column='sentence',\n label_column='label',\n model_spec=mb_spec,\n is_training=True)\ntest_data = DataLoader.from_csv(\n filename='dev.csv',\n text_column='sentence',\n label_column='label',\n model_spec=mb_spec,\n is_training=False)\nExplanation: Load training data\nYou can upload your own dataset to work through this tutorial. Upload your dataset by using the left sidebar in Colab.\n
![\"Upload](\"https://storage.googleapis.com/download.tensorflow.org/models/tflite/screenshots/model_maker_text_classification.png\")
\nIf you prefer not to upload your dataset to the cloud, you can also locally run the library by following the guide.\nTo keep it simple, we will reuse the SST-2 dataset downloaded earlier. Let's use the DataLoader.from_csv method to load the data.\nPlease be noted that as we have changed the model architecture, we will need to reload the training and test dataset to apply the new preprocessing logic.\nEnd of explanation\nmodel = text_classifier.create(train_data, model_spec=mb_spec, epochs=3)\nExplanation: The Model Maker library also supports the from_folder() method to load data. It assumes that the text data of the same class are in the same subdirectory and that the subfolder name is the class name. Each text file contains one movie review sample. The class_labels parameter is used to specify which the subfolders.\nTrain a TensorFlow Model\nTrain a text classification model using the training data.\nNote: As MobileBERT is a complex model, each training epoch will takes about 10 minutes on a Colab GPU. Please make sure that you are using a GPU runtime.\nEnd of explanation\nmodel.summary()\nExplanation: Examine the detailed model structure.\nEnd of explanation\nloss, acc = model.evaluate(test_data)\nExplanation: Evaluate the model\nEvaluate the model that we have just trained using the test data and measure the loss and accuracy value.\nEnd of explanation\nmodel.export(export_dir='mobilebert/')\nExplanation: Export as a TensorFlow Lite model\nConvert the trained model to TensorFlow Lite model format with metadata so that you can later use in an on-device ML application. The label file and the vocab file are embedded in metadata. The default TFLite filename is model.tflite.\nIn many on-device ML application, the model size is an important factor. Therefore, it is recommended that you apply quantize the model to make it smaller and potentially run faster.\nThe default post-training quantization technique is dynamic range quantization for the BERT and MobileBERT models.\nEnd of explanation\nmodel.export(export_dir='mobilebert/', export_format=[ExportFormat.LABEL, ExportFormat.VOCAB])\nExplanation: The TensorFlow Lite model file can be integrated in a mobile app using the BertNLClassifier API in TensorFlow Lite Task Library. Please note that this is different from the NLClassifier API used to integrate the text classification trained with the average word vector model architecture.\nThe export formats can be one or a list of the following:\nExportFormat.TFLITE\nExportFormat.LABEL\nExportFormat.VOCAB\nExportFormat.SAVED_MODEL\nBy default, it exports only the TensorFlow Lite model file containing the model metadata. You can also choose to export other files related to the model for better examination. For instance, exporting only the label file and vocab file as follows:\nEnd of explanation\naccuracy = model.evaluate_tflite('mobilebert/model.tflite', test_data)\nprint('TFLite model accuracy: ', accuracy)\nExplanation: You can evaluate the TFLite model with evaluate_tflite method to measure its accuracy. Converting the trained TensorFlow model to TFLite format and apply quantization can affect its accuracy so it is recommended to evaluate the TFLite model accuracy before deployment.\nEnd of explanation\nnew_model_spec = model_spec.get('mobilebert_classifier')\nnew_model_spec.seq_len = 256\nExplanation: Advanced Usage\nThe create function is the driver function that the Model Maker library uses to create models. The model_spec parameter defines the model specification. The AverageWordVecSpec and BertClassifierSpec classes are currently supported. The create function comprises of the following steps:\nCreates the model for the text classifier according to model_spec.\nTrains the classifier model. The default epochs and the default batch size are set by the default_training_epochs and default_batch_size variables in the model_spec object.\nThis section covers advanced usage topics like adjusting the model and the training hyperparameters.\nCustomize the MobileBERT model hyperparameters\nThe model parameters you can adjust are:\nseq_len: Length of the sequence to feed into the model.\ninitializer_range: The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\ntrainable: Boolean that specifies whether the pre-trained layer is trainable.\nThe training pipeline parameters you can adjust are:\nmodel_dir: The location of the model checkpoint files. If not set, a temporary directory will be used.\ndropout_rate: The dropout rate.\nlearning_rate: The initial learning rate for the Adam optimizer.\ntpu: TPU address to connect to.\nFor instance, you can set the seq_len=256 (default is 128). This allows the model to classify longer text.\nEnd of explanation\nnew_model_spec = AverageWordVecSpec(wordvec_dim=32)\nExplanation: Customize the average word embedding model hyperparameters\nYou can adjust the model infrastructure like the wordvec_dim and the seq_len variables in the AverageWordVecSpec class.\nFor example, you can train the model with a larger value of wordvec_dim. Note that you must construct a new model_spec if you modify the model.\nEnd of explanation\nnew_train_data = DataLoader.from_csv(\n filename='train.csv',\n text_column='sentence',\n label_column='label',\n model_spec=new_model_spec,\n is_training=True)\nExplanation: Get the preprocessed data.\nEnd of explanation\nmodel = text_classifier.create(new_train_data, model_spec=new_model_spec)\nExplanation: Train the new model.\nEnd of explanation\nmodel = text_classifier.create(new_train_data, model_spec=new_model_spec, epochs=20)\nExplanation: Tune the training hyperparameters\nYou can also tune the training hyperparameters like epochs and batch_size that affect the model accuracy. For instance,\nepochs: more epochs could achieve better accuracy, but may lead to overfitting.\nbatch_size: the number of samples to use in one training step.\nFor example, you can train with more epochs.\nEnd of explanation\nnew_test_data = DataLoader.from_csv(\n filename='dev.csv',\n text_column='sentence',\n label_column='label',\n model_spec=new_model_spec,\n is_training=False)\nloss, accuracy = model.evaluate(new_test_data)\nExplanation: Evaluate the newly retrained model with 20 training epochs.\nEnd of explanation\nspec = model_spec.get('bert_classifier')\nExplanation: Change the Model Architecture\nYou can change the model by changing the model_spec. The following shows how to change to BERT-Base model.\nChange the model_spec to BERT-Base model for the text classifier.\nEnd of explanation"}}},{"rowIdx":2977,"cells":{"Unnamed: 0":{"kind":"number","value":2977,"string":"2,977"},"text_prompt":{"kind":"string","value":"Given the following text description, write Python code to implement the functionality described below step by step\nDescription:\n Pandas 数据读写\nAPI\n读取 | 写入 \n--- | ---\nread_csv | to_csv\nread_excel | to_excel\nread_hdf | to_hdf\nread_sql | to_sql\nread_json | to_json\nread_html | to_html\nread_stata | to_stata\nread_clipboard | to_clipboard\nread_pickle | to_pickle\nCVS 文件读写\ncsv 文件内容\nwhite,read,blue,green,animal\n1,5,2,3,cat\n2,7,8,5,dog\n3,3,6,7,horse\n2,2,8,3,duck\n4,4,2,1,mouse\nStep1: 读取没有head的数据\n1,5,2,3,cat\n2,7,8,5,dog\n3,3,6,7,horse\n2,2,8,3,duck\n4,4,2,1,mouse\nStep2: 可以指定header\nStep3: 创建一个具有等级结构的DataFrame对象，可以添加index_col选项,数据文件格式\ncolors,status,item1,item2,item3\nblack,up,3,4,6\nblack,down,2,6,7\nwhite,up,5,5,5\nwhite,down,3,3,2\nred,up,2,2,2\nred,down,1,1,4\nStep4: Regexp 解析TXT文件\n使用正则表达式指定sep,来达到解析数据文件的目的。\n正则元素 | 功能\n--- | ---\n. | 换行符以外所有元素\n\\d | 数字\n\\D | 非数字\n\\s | 空白字符\n\\S | 非空白字符\n\\n | 换行符\n\\t | 制表符\n\\uxxxx | 使用十六进制表示ideaUnicode字符 \n数据文件随机以制表符和空格分隔\nwhite red blue green\n1 4 3 2\n2 4 6 7\nStep5: 读取有字母分隔的数据\n000end123aaa122\n001end125aaa144\nStep6: 读取文本文件跳过一些不必要的行\n```\nlog file\nthis file has been generate by automatic system\nwhite,red,blue,green,animal\n12-feb-2015\nStep7: 从TXT文件中读取部分数据\n只想读文件的一部分，可明确指定解析的行号，这时候用到nrows和skiprows选项，从指定的行开始和从起始行往后读多少行（norow=i)\nStep8: 实例 ：\n对于一列数据，每隔两行取一个累加起来，最后把和插入到列的Series对象中\nStep9: 写入文件\nto_csv(filenmae)\nto_csv(filename,index=False,header=False)\nto_csv(filename,na_rep='NaN')\nHTML文件读写\n写入HTML文件\nStep10: 创建复杂的DataFrame\nStep11: HTML读表格\nStep12: 读写xml文件\n使用的第三方的库 lxml\nStep13: 读写Excel文件\nStep14: JSON数据\nStep15: HDF5数据\nHDF文件（hierarchical data from)等级数据格式，用二进制文件存储数据。\nStep16: pickle数据\nStep17: 数据库连接\n以sqlite3为例介绍"},"code_prompt":{"kind":"string","value":"Python Code:\nimport numpy as np\nimport pandas as pd\ncsvframe=pd.read_csv('myCSV_01.csv')\ncsvframe\n# 也可以通过read_table来读写数据\npd.read_table('myCSV_01.csv',sep=',')\nExplanation: Pandas 数据读写\nAPI\n读取 | 写入 \n--- | ---\nread_csv | to_csv\nread_excel | to_excel\nread_hdf | to_hdf\nread_sql | to_sql\nread_json | to_json\nread_html | to_html\nread_stata | to_stata\nread_clipboard | to_clipboard\nread_pickle | to_pickle\nCVS 文件读写\ncsv 文件内容\nwhite,read,blue,green,animal\n1,5,2,3,cat\n2,7,8,5,dog\n3,3,6,7,horse\n2,2,8,3,duck\n4,4,2,1,mouse\nEnd of explanation\npd.read_csv('myCSV_02.csv',header=None)\nExplanation: 读取没有head的数据\n1,5,2,3,cat\n2,7,8,5,dog\n3,3,6,7,horse\n2,2,8,3,duck\n4,4,2,1,mouse\nEnd of explanation\npd.read_csv('myCSV_02.csv',names=['white','red','blue','green','animal'])\nExplanation: 可以指定header\nEnd of explanation\npd.read_csv('myCSV_03.csv',index_col=['colors','status'])\nExplanation: 创建一个具有等级结构的DataFrame对象，可以添加index_col选项,数据文件格式\ncolors,status,item1,item2,item3\nblack,up,3,4,6\nblack,down,2,6,7\nwhite,up,5,5,5\nwhite,down,3,3,2\nred,up,2,2,2\nred,down,1,1,4\nEnd of explanation\npd.read_csv('myCSV_04.csv',sep='\\s+')\nExplanation: Regexp 解析TXT文件\n使用正则表达式指定sep,来达到解析数据文件的目的。\n正则元素 | 功能\n--- | ---\n. | 换行符以外所有元素\n\\d | 数字\n\\D | 非数字\n\\s | 空白字符\n\\S | 非空白字符\n\\n | 换行符\n\\t | 制表符\n\\uxxxx | 使用十六进制表示ideaUnicode字符 \n数据文件随机以制表符和空格分隔\nwhite red blue green\n1 4 3 2\n2 4 6 7\nEnd of explanation\npd.read_csv('myCSV_05.csv',sep='\\D*',header=None,engine='python')\nExplanation: 读取有字母分隔的数据\n000end123aaa122\n001end125aaa144\nEnd of explanation\npd.read_table('myCSV_06.csv',sep=',',skiprows=[0,1,3,6])\nExplanation: 读取文本文件跳过一些不必要的行\n```\nlog file\nthis file has been generate by automatic system\nwhite,red,blue,green,animal\n12-feb-2015:counting of animals inside the house\n1,3,5,2,cat\n2,4,8,5,dog\n13-feb-2015:counting of animals inside the house\n3,3,6,7,horse\n2,2,8,3,duck\n```\nEnd of explanation\npd.read_csv('myCSV_02.csv',skiprows=[2],nrows=3,header=None)\nExplanation: 从TXT文件中读取部分数据\n只想读文件的一部分，可明确指定解析的行号，这时候用到nrows和skiprows选项，从指定的行开始和从起始行往后读多少行（norow=i)\nEnd of explanation\nout = pd.Series()\ni=0\npieces = pd.read_csv('myCSV_01.csv',chunksize=3)\nfor piece in pieces:\n print piece\n out.set_value(i,piece['white'].sum())\n i += 1\nout\nExplanation: 实例 ：\n对于一列数据，每隔两行取一个累加起来，最后把和插入到列的Series对象中\nEnd of explanation\nframe = pd.DataFrame(np.arange(4).reshape((2,2)))\nprint frame.to_html()\nExplanation: 写入文件\nto_csv(filenmae)\nto_csv(filename,index=False,header=False)\nto_csv(filename,na_rep='NaN')\nHTML文件读写\n写入HTML文件\nEnd of explanation\nframe = pd.DataFrame(np.random.random((4,4)),\n index=['white','black','red','blue'],\n columns=['up','down','left','right'])\nframe\ns = ['']\ns.append('