# Working with the embeddings
The embeddings are available as numpy files, where each row is a 768-dimension floating point embedding. Each row is associated with it's matching index in the metadata files.

#### Open an embedding file
The files are in numpy format, and can be opened with `numpy` in Python. 
```python
import numpy as np
embeddings = np.load('pd12m.01.npy')
```

#### Join Embeddings to Metadata
If you already have the metadata files loaded with pandas, you can join the embeddings to the dataframe  simply.
```python
df["embeddings"] = embeddings.tolist()
```
Alternatively, you could use a 0-based index to access both the metadata and associated embedding.
```python
i = 300
metadata = df.iloc[i]
embedding = embeddings[i]