Erstellt ein NLP-Modell zur Analyse des spanischen Textgefühls mit TensorFlow.
To create a model of NLP for analysis of the feeling of Spanish texts using TensorFlow, the following steps can be followed: 1. Preprocessing of data: The first step to create a model of NLP is to preprocess the data. This means cleaning the text of scores, numbers, symbols and empty words (stopwords), and then tokening the text. Also, you can apply techniques of stempming or mottoization to reduce the words to their root. 2. Creating the model: The next step is to create the model. For analysis of the feeling, you can use a recurrent neuronal network (RNN) or a convolutional neural network (CNN). In this case, you can use a CNN. 3. Training of the model: Once the model has been created, you proceed to train it with a set of training data. To do this, you must label the training data as positive or negative, and then feed them to the model to learn to classify the texts according to their feeling. 4. Evaluation of the model: After the model has been created, you should be trained with a set of training data model. This will allow you to label you to label the new model to label model. This will allow, oov_token="<OOV>") tokenizer.fit_on_texts(tentences_train) word_index = tokenizer.word_index # Training and test sequences sequences sequences_train =train =tokenizer.texts_to_sequences(tents_train) paded_train = pad_sequences(sequences_train, maxlen=100, padding=post, truncating=post) sequences_test =tokenizer.texts_to_sequences(tents_teast) padd_tested_test) padded_test = pad_sequences(models_test, maxlen=100, padding=post, truncating=t), truncating=post, truncating=post=post=post=post=post) # Model model creation model = tf.keras.Sedential([ tf.keras.layers.Embeding(5000, 32, input_input_len_len=100, heading_lung,The model is trained with a training data set and evaluated with a relu activation function, a maximum pooling layer, a LSTM layer and a dense layer with a sigmoid activation function +? This code uses a CNN with an embedding layer, a dropout layer, a layer with a relulu activation function, a maximum pooling layer, a maximum pooling layer, a LSTM layer, and a dense layer with a sigmoid activation function. The model is trained with a training data set, a dropout layer, a layer with a relu activation function, a maximum pooling layer, a LSTM layer and a layer with a sigmoidal activation function. The model is trained with a training data set, a layer with a relu activation function, a maximum pooling layer, a LSTM layer, a LSTM layer and a layer with a dense layer with a sigmoidal activation function. The model is trained with a training data set of training and is evaluated with a new data set of test model. Finally, the model is used to predict the model is used for the test data set. Finally, the model, the