# [Anchor](https://qdrant.tech/documentation/fastembed/fastembed-quickstart/\#how-to-generate-text-embedings-with-fastembed) How to Generate Text Embedings with FastEmbed

## [Anchor](https://qdrant.tech/documentation/fastembed/fastembed-quickstart/\#install-fastembed) Install FastEmbed

```python
pip install fastembed

```

Just for demo purposes, you will use Lists and NumPy to work with sample data.

```python
from typing import List
import numpy as np

```

## [Anchor](https://qdrant.tech/documentation/fastembed/fastembed-quickstart/\#load-default-model) Load default model

In this example, you will use the default text embedding model, `BAAI/bge-small-en-v1.5`.

```python
from fastembed import TextEmbedding

```

## [Anchor](https://qdrant.tech/documentation/fastembed/fastembed-quickstart/\#add-sample-data) Add sample data

Now, add two sample documents. Your documents must be in a list, and each document must be a string

```python
documents: List[str] = [\
    "FastEmbed is lighter than Transformers & Sentence-Transformers.",\
    "FastEmbed is supported by and maintained by Qdrant.",\
]

```

Download and initialize the model. Print a message to verify the process.

```python
embedding_model = TextEmbedding()
print("The model BAAI/bge-small-en-v1.5 is ready to use.")

```

## [Anchor](https://qdrant.tech/documentation/fastembed/fastembed-quickstart/\#embed-data) Embed data

Generate embeddings for both documents.

```python
embeddings_generator = embedding_model.embed(documents)
embeddings_list = list(embeddings_generator)
len(embeddings_list[0])

```

Here is the sample document list. The default model creates vectors with 384 dimensions.

```bash
Document: This is built to be faster and lighter than other embedding libraries e.g. Transformers, Sentence-Transformers, etc.
Vector of type: <class 'numpy.ndarray'> with shape: (384,)
Document: fastembed is supported by and maintained by Qdrant.
Vector of type: <class 'numpy.ndarray'> with shape: (384,)

```

## [Anchor](https://qdrant.tech/documentation/fastembed/fastembed-quickstart/\#visualize-embeddings) Visualize embeddings

```python
print("Embeddings:\n", embeddings_list)

```

The embeddings don’t look too interesting, but here is a visual.

```bash
Embeddings:
 [[-0.11154681  0.00976555  0.00524559  0.01951888 -0.01934952  0.02943449\
  -0.10519084 -0.00890122  0.01831438  0.01486796 -0.05642502  0.02561352\
  -0.00120165  0.00637456  0.02633459  0.0089221   0.05313658  0.03955453\
  -0.04400245 -0.02929407  0.04691846 -0.02515868  0.00778646 -0.05410657\
...\
  -0.00243012 -0.01820582  0.02938612  0.02108984 -0.02178085  0.02971899\
  -0.00790564  0.03561783  0.0652488  -0.04371546 -0.05550042  0.02651665\
  -0.01116153 -0.01682246 -0.05976734 -0.03143916  0.06522726  0.01801389\
  -0.02611006  0.01627177 -0.0368538   0.03968835  0.027597    0.03305927]]

```

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