It doesn’t mean that “anti-racism” training based on Robin DiAngelo-style “all whites are racist until proven innocent”

We’ll first download a large dataset with 500K rows. Then we’ll convert a text column to an 300d embedding vector by using the pre-trained fasttext sentence vector. Then I’ll train both KNN and HNSW ANN models on different lengths of the input data [1000, 10000, 100000, len(data)] to measure the impact of data size on the speed. Finally, I’ll query K = 10 and 100 nearest neighbors from both models to measure the impact of K on the speed. First, let’s import the necessary packages and models. This will take some time as the fasttext model needs to be downloaded from the internet.

# Controlling the recall by setting ef # ef should always be > k p.set_ef(ef) # If you want to save the graph to a file if save_index_file: p.save_index(save_index_file) return p

# Pre-Processing: https://colab.research.google.com/drive/1Zv6MARGQcrBbLHyjPVVMZVnRWsRnVMpV#scrollTo=LgWrDtZ94w89 # Convert list into pandas dataframe df = pd.DataFrame.from_dict(data) df.fillna(', inplace=True)

Trumpism is odious. But anti-Trumpism can pull in the direction of a knee-jerk “anything opposed by Trump and/or Trumpists must be fine” mentality and of reluctance to criticize the left for fear of enabling the right.

When a Black woman is victimized, it can easily go unnoticed. Our communities have to march just to start the wheels of justice against the perpetrators of wrongful acts and murders committed against us. But even if we are successful enough to get an arrest or conviction when one of our own has been killed or goes missing, the attention span of White America is so short that our devastation is easily forgotten. Those who see no value in Black women quickly “turn off” and turn away from news that doesn’t involve their immediate circles.

The fact that a Black woman would even need to consider turning a deaf ear and blind eye to egregious acts committed against her community simply because the frequency of those events was harmful to her mental health further punctuates the sad state of affairs in this country. That blind eye is often the only thing that saves us from coming face to face with the fact that America was never designed to protect and respect the Black woman.

But eventually, the stories catch up with us one way or another. When this happens, it can create emotions as strong as the pressure caused by the release of water from a broken levee. And like the water, the emotions are impossible to contain and come with the capability of damaging all aspects of our lives. The tragedy is that we aren’t even responsible for the events that led to our suffering, and those who inflicted the pain are the first to ignore our gaping wounds.

Now that we have the input to our algorithms, let’s run the benchmarking tests. We will run the test as a loop within a loop of the number of products in the search space and K nearest neighbors being searched.

def fit_hnsw_index(features, ef=100, M=16, save_index_file=False): # Convenience function to create HNSW graph # features : list of lists containing the embeddings # ef, M: parameters to tune the HNSW algorithm num_elements = len(features) labels_index = np.arange(num_elements)

Beware! The whole test ran for ~6days on an 8 core, 30 GB RAM machine running 24x7, so this could take some time. Ideally, you could speed it up by multiprocessing since each run is independent of the other.

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at the BuildConfig on your app module is useful. It has the app version code, version name, application id and any custom flag you decided to add. So, I recommend to keep it (but remember to avoid using BuildConfig.DEBUG). So, to override the default behavior defined on gradle.properties, just add the following configuration to yourapp/build.gradle

At each iteration, in addition to clocking the time taken by each algorithm, we check the pct_overlap as the ratio of the number of KNN nearest neighbors that were also picked up as nearest neighbors by the ANN.

Although the dismissal of acts of violence against Black women is certainly nothing new, this recent despicable act by GOP members is a frustrating and painful reminder of how our lives rank among the lowest priorities of most of White America. Breonna Taylor was the daughter of a woman who loved her unconditionally and has grieved just as much as any other mother in the world who lost a child. Her pain isn’t any less just because she is a Black woman. Does she not deserve the same outpouring of compassion and kindness from all Americans that was given to the mothers of Laci Peterson and Natalee Holloway? Was Breonna Taylor’s life not worth recognizing? Was she not worth acknowledging? Did she not deserve to be mourned and revered like other women of the dominate society who also lost their lives?

To run any similarity search on textual data, we must first convert it to a numeric vector. A fast and convenient approach uses a pre-trained network’s embedding layer, such as the one provided by Facebook’s [FastText]. Since we want all the rows to have the same length vector, irrespective of the number of words in the title, we shall apply the get_sentence_vector method on the title column in df. Once the embedding is completed, we extract the emb column as a list of lists to input into our NN algorithms. Ideally, you would run some text cleaning pre-processing before this step. Also, using a fine-tuned embedding model is generally a good idea.

We’ll use the [Amazon product dataset], which contains 527000 products in the ‘Cell Phones