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Last active September 27, 2025 06:53
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Embeddings
$ python3 -m venv ~/.embeddings
$ source ~/.embeddings/bin/activate
$ pip install -U \
    sentence-transformers \
    pandas \
    umap-learn
$ wget https://gist.githubusercontent.com/trag1c/f74b2ab3589bc4ce5706f934616f6195/raw/5aa7de70fc83664017cb97dd02cbf6dc76b9e4a3/nouns.txt
$ wc -l nouns.txt # 40,940 lines, 378 KB
$ head nouns.txt
'hood
a
a'man
a-horizon
a-line
a-list
a-team
aa
aachen
aalborg
import numpy as np
import pandas as pd
from   sentence_transformers import SentenceTransformer
from   sklearn.decomposition import PCA
from   sklearn.manifold import TSNE
import umap.umap_ as umap


model = SentenceTransformer('intfloat/multilingual-e5-large-instruct')
df    = pd.read_csv('nouns.txt', header=None, names=['noun'])
embeddings       = model.encode(df['noun'].tolist(), device='cpu') # ~5 minutes
df['embedding']  = list(embeddings)
embeddings_array = np.array(embeddings)

# Initialize UMAP to reduce to 2 components
# You might need to adjust parameters like n_neighbors and min_dist
reducer         = umap.UMAP(n_components=2,
                            random_state=42,
                            n_neighbors=15,
                            min_dist=0.1)
embeddings_umap = reducer.fit_transform(embeddings_array)
df['umap_x']    = embeddings_umap[:, 0]
df['umap_y']    = embeddings_umap[:, 1]

# Initialize t-SNE to reduce to 2 components
# You might need to adjust parameters like n_iter and
# perplexity depending on your data
tsne            = TSNE(n_components=2,
                       random_state=42,
                       max_iter=300,
                       perplexity=30)
embeddings_tsne = tsne.fit_transform(embeddings_array)
df['tsne_x']    = embeddings_tsne[:, 0]
df['tsne_y']    = embeddings_tsne[:, 1]

# Initialize PCA to reduce to 2 components
pca           = PCA(n_components=2)
embeddings_2d = pca.fit_transform(embeddings_array)
df['pca_x']   = embeddings_2d[:, 0]
df['pca_y']   = embeddings_2d[:, 1]

df.to_json('english_nouns_embeddings.json',
           index=False,
           orient='records',
           lines=True)

The above JSON is 543 MB uncompressed.

$ head -n1 english_nouns_embeddings.json | jq -S .
{
  "embedding": [
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