The model trained on the test data gave a decent accuracy of around 87%. In this article, we have discussed the details and implementation of IMDb dataset using Keras Library. Graph star and BERT large finetune UDA are near contenders with a precision of around 96%. The present state of the art on IMDb dataset is NB-weighted-BON + dv-cosine. We finished with an accuracy of 87.25% on the test dataset. Print("Model accuracy on the IMDb dataset: %".format(scores*100)) model.fit(X_train, y_train, epochs=5, batch_size=64) scores = model.evaluate(X_test, y_test, verbose=0) Our model gave an accuracy of 92.88% on training data. Next Step is to train the model with epoch=5 and batch size=64. pile(loss='binary_crossentropy', optimizer='adam', metrics=) Model.add(Dense(1, activation='sigmoid')) Model.add(Embedding(max_words, embedding_vector_length, input_length=max_review_length)) The sigmoid function will choose if the data ought to be given a 1 (positive)or a – 1(negative). We will add a Dense layer to the furthest limit of our model and utilize a sigmoid function capacity to deliver good results. LSTM Layer decides which words in the reviews are important that will flow through them. The Embedding layer turns each of the words into vectors of 32 digits. We are adding the model=Sequential() line so that the data will flow from input to output in a sequence way. X_test = sequence.pad_sequences(X_test, maxlen=max_review) X_train = sequence.pad_sequences(X_train, maxlen=max_review) Suppose a review has a length shorter than 500 pad_sequence will add “0” to the remaining length.įor example “Bangalore 0 0 0 0” max_review = 500 If the length of the review is more than 500, shorten it to maximum length. Let’s define the maximum length of the review. (X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=maximum_words) Ensure that the maximum number of words is 5000. Load the information from the IMDb dataset and split it into a train and test set. Import all the libraries required for this project.įrom import Embedding The dataset can be downloaded from the following link. Return tuple(x) Code Implementation using Keras Library With open(filename, 'r', encoding="utf-8") as f: def imdb_dataset(directory='data/',ĭownload_file_maybe_extract(url=url, directory=directory, check_files=check_files)ĭir_ for (requested, dir_) in įull_path = os.path.join(directory, extracted_name, split_directory)įor filename in glob.iglob(os.path.join(full_path, sentiment, '*.txt')): The list x defined below will contain reviews with its polarity. Loading the dataset Using Pytorch import osįrom torchnlp.download import download_file_maybe_extractĭefine the parameters that need to be passed to the function. In every one of these folders, there are numerous TXT records containing the substance of the film survey, with each document containing one report. In each of the directories contained in the sets, there are another two directories representing pos and neg tags, to partition the information through various marks. The reviews were then evenly divided into training and test sets uploaded to their website. They searched the content information present in each of the reviews and discovered any highlights that were representative for judging whether the review was positive or negative. The raw data was collected by the researchers from the IMDb website. Further, we will implement the IMDB dataset using Keras Library. Here, we will examine the information contained in this dataset, how it was gathered, and give some benchmark models that gave high accuracy on this dataset. Neutral reviews were excluded from this dataset. The training set contains 25000 reviews so as the test set.Ī negative review has a score of ≤ 4 out of 10, and a positive survey has a score of ≥ 7 out of 10. The dataset was evenly divided into training and test sets. Ng, and Christopher Potts of Stanford University. It was developed in 2011 by the researchers: Andrew L. The IMDb dataset contains 50,000 surveys, permitting close to 30 audits for each film. The data which is introduced on the IMDb portal incorporates cast, creation group, director crew, individual accounts, plot outlines, random data, evaluations, fan, and critics reviews. Internet Movie Database (IMDb) is an online information base committed to a wide range of data about a wide scope of film substance, for example, movies, TV and web-based streaming shows, etc.
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