When counting cooccurring pairs, use the created vocabulary to convert the corpus, which is a list of tokens, into integer indices.Create a vocabulary from the corpus, which consists of the top k most frequent tokens.Now we have the Vocabulary class, the remaining question is: how do we use it? There are basically two use cases: For more information about dataclass, please refer to the official documentation. For example, token2index is default to an empty dict by setting default_factory=dict. I can also set default value for fields when defining them. With this feature, I only need to define the fields with type annotation, and the _init_() method is automatically generated for me. With this understanding in mind, we can now look at the code:įor the class implementation, I make use of Python’s dataclass feature. The reason why this method is needed will be uncovered later, when we actually count cooccurring pairs. shuffle(): Randomly shuffle all the tokens so that the mapping between tokens and indices is randomized.get_topk_subset(k): Create a new vocabulary with the top k most frequent tokens.get_token(index): Return the token corresponding to the index.get_index(token): Return the index of the token.If previously unseen, a new index is generated. add(token): Add a new token into the vocabulary._unk_token: An integer used as the index for unknown tokens.token_counts: A list where the ith value is the count of the token with index i.
WORDS CONTAINING PHEW CODE
Then for the rest of the code, we can use the parameters as config.batch_size, config.learning_rate instead of hard coded values, which also makes the code nicer.Īnd that’s all the preparation work needed. In the code, also add a loading function to load configuration from the yaml file, like this:Ĭode snippet for loading configuration from config.yaml Based on my experience, I find the best way is to store all of them in a single yaml file with the name config.yaml. These parameters can incur lots of overhead if you don’t manage them well.
When working on a machine learning model, there is always a wide range of parameters to configure, such as data file path, batch size, word embedding size, etc. We only want a list of all the words, so flatten it with itertools: import itertools corpus = list(_iterable(dataset)) The dataset is a list of lists, where each sublist is a list of words representing a sentence. To get it, we can use the gensim downloader: import gensim.downloader as api dataset = api.load("text8") Step 0: Preparation Training Dataįor this project, I use the Text8 Dataset as the training data. The following sections walk through the implementation details step by step. So I carried out a comprehensive Python implementation of the model, which aligns with the goal of training a huge vocabulary with only a single machine. The released code was written in C, which can be somewhat unfamiliar for NLP learners. For more information about the underlying theory, please refer to the original paper.Īccording to the paper, the GloVe model was trained with a single machine. As stated before, the focus is purely on the implementation. This post is the first of this series, which reproduces the GloVe model based on the original paper. I also created a GitHub repository for this effort.
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Thus I decided to start a series of posts focusing on implementing classical NLP papers. As far as I know, many NLP learners have run into the same situation as me. But when it comes to reproducing them, difficulties emerge. Reading paper is fun, and give me the illusion that I have mastered a wide range of techniques. As an NLP data scientist, I frequently read papers with topics varying from word vectors, RNNs, and transformers.
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