resource
https://huggingface.co/nevmenandr/w2v-russian-tolstoy
model
nevmenandr/w2v-russian-tolstoy
https://huggingface.co/nevmenandr/w2v-russian-tolstoy
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code_fence
```python
import gensim
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

import seaborn as sns
sns.set_style("darkgrid")

from sklearn.decomposition import PCA
from sklearn.manifold import TSNE

modelLNT2 = Word2Vec.load("cbow_300_10.model")

# skip some code... for full version see model's card

tsnescatterplot(modelLNT2, 'жизнь_S', [i[0] for i in modelLNT2.wv.most_similar(negative=["жизнь_S"])])
```
import gensim
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

import seaborn as sns
sns.set_style("darkgrid")

from sklearn.decomposition import PCA
from sklearn.manifold import TSNE

modelLNT2 = Word2Vec.load("cbow_300_10.model")

# skip some code... for full version see model's card

tsnescatterplot(modelLNT2, 'жизнь_S', [i[0] for i in modelLNT2.wv.most_similar(negative=["жизнь_S"])])
python
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text
life by Tolstoy (w2v):
life by Tolstoy (w2v):
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https://huggingface.co/nevmenandr/w2v-russian-tolstoy

```python
import gensim
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

import seaborn as sns
sns.set_style("darkgrid")

from sklearn.decomposition import PCA
from sklearn.manifold import TSNE

modelLNT2 = Word2Vec.load("cbow_300_10.model")

# skip some code... for full version see model's card

tsnescatterplot(modelLNT2, 'жизнь_S', [i[0] for i in modelLNT2.wv.most_similar(negative=["жизнь_S"])])
```

life by Tolstoy (w2v):

