- [47+ classification and regression algorithms](https://postgresml.org/docs/guides/training/algorithm_selection)

For more details, take a look at our [Quick Start with Docker](https://postgresml.org/docs/guides/developer-docs/quick-start-with-docker) documentation.

Please see the [quick start instructions](https://postgresml.org/docs/guides/getting-started/sign-up) for general information on installing or deploying PostgresML. A [developer guide](https://postgresml.org/developer_guide/overview/) is also available for those who would like to contribute.

PostgresML provides a simple interface to generate embeddings from text in your database. You can use the [`pgml.embed`](https://postgresml.org/docs/guides/transformers/embeddings) function to generate embeddings for a column of text. The function takes a transformer name and a text value. The transformer will automatically be downloaded and cached on your connection process for reuse. You can see a list of potential good candidate models to generate embeddings on the [Massive Text Embedding Benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard).

Model quality is often a trade-off between compute resources and incremental quality improvements. Sometimes a few thousands training examples and an off the shelf algorithm can deliver significant business value after a few seconds of training. PostgresML allows stakeholders to choose several [different algorithms](/docs/guides/training/algorithm_selection/) to get the most bang for the buck, or invest in more computationally intensive techniques as necessary. In addition, PostgresML can automatically apply best practices for [data cleaning](/docs/guides/training/preprocessing/)) like imputing missing values by default and normalizing features to prevent common problems in production.

PostgresML comes with a web app to provide visibility into models and datasets in your database. If you're running [our Docker container](/docs/guides/developer-docs/quick-start-with-docker), you can view it running on [http://localhost:8000/](http://localhost:8000/).

If you've already been through the [Training Overview](/docs/guides/training/overview/), you can see the results of those efforts:

Take a look at [Deploying Models](/docs/guides/predictions/deployments/) documentation for more details.

Deployments are an artifact of calls to `pgml.deploy()` and `pgml.train()`. See [Deployments](/docs/guides/predictions/deployments/) for ways to create new deployments manually.

Models are an artifact of calls to `pgml.train()`. See [Training Overview](/docs/guides/training/overview/) for ways to create new models.

Projects are an artifact of calls to `pgml.train()`. See [Training Overview](/docs/guides/training/overview/) for ways to create new projects.

Snapshots are an artifact of calls to `pgml.train()` that specify the `relation_name` and `y_column_name` parameters. See [Training Overview](/docs/guides/training/overview/) for ways to create new snapshots.

If you're using our <a href="/docs/guides/developer-docs/quick-start-with-docker">Docker</a> stack, you can import the data there:</p>

GPU setup for Tensorflow is covered in the [documentation](https://www.tensorflow.org/install/pip). You may acquire pre-trained GPU enabled models for fine tuning from [Hugging Face](/docs/guides/transformers/fine_tuning/).

GPU setup for Torch is covered in the [documentation](https://pytorch.org/get-started/locally/). You may acquire pre-trained GPU enabled models for fine tuning from [Hugging Face](/docs/guides/transformers/fine_tuning/).

GPU setup for Flax is covered in the [documentation](https://github.com/google/jax#pip-installation-gpu-cuda). You may acquire pre-trained GPU enabled models for fine tuning from [Hugging Face](/docs/guides/transformers/fine_tuning/).

With the release of PostgresML 2.0, this documentation has been deprecated. New installation instructions are <a href="/docs/guides/setup/v2/installation/">available</a>.

Check out our [Training](/docs/guides/training/overview/) and [Predictions](/docs/guides/predictions/overview/) documentation for more details. Some more advanced topics like [hyperparameter search](/docs/guides/training/hyperparameter_search/) and [GPU acceleration](/docs/guides/setup/gpu_support/) are available as well.

If you're just looking to try PostgresML without installing it on your system, take a look at our [Quick Start with Docker](/docs/guides/developer-docs/quick-start-with-docker) guide.

Make sure you've set up the system requirements in [v2.0 installation](/docs/guides/setup/v2/installation/), so that the v2.0 extension may be installed.

You may pass any of the arguments listed in the algorithms documentation as hyperparameters. See [Algorithms](/docs/guides/training/algorithm_selection/) for the complete list of algorithms and their associated hyperparameters.

| `algorithm` | The algorithm to train on the dataset, see [Algorithm Selection](/docs/guides/training/algorithm_selection/) for details. | `xgboost` |

| `search` | If set, PostgresML will perform a hyperparameter search to find the best hyperparameters for the algorithm. See [Hyperparameter Search](/docs/guides/training/hyperparameter_search/) for details. | `grid` |

Now that we've got data, we're ready to train a model using an algorithm. We'll start with the default `linear` algorithm to demonstrate the basics. See the [Algorithms](/docs/guides/training/algorithm_selection/) for a complete list of available algorithms.

The output gives us information about the training run, including the `deployed` status. This is great news indicating training has successfully reached a new high score for the project's key metric and our new model was automatically deployed as the one that will be used to make new predictions for the project. See [Deployments](/docs/guides/predictions/deployments/) for a guide to managing the active model.

These preprocessing steps may be specified on a per-column basis to the [train()](/docs/guides/training/overview/) function. By default, PostgresML does minimal preprocessing on training data, and will raise an error during analysis if NULL values are encountered without a preprocessor. All types other than `TEXT` are treated as quantitative variables and cast to floating point representations before passing them to the underlying algorithm implementations.

See [text classification documentation](https://huggingface.co/tasks/text-classification) for more options and potential use cases beyond sentiment analysis. You'll notice the outputs are not great in this example. RoBERTa is a breakthrough model, that demonstrated just how important each particular hyperparameter is for the task and particular dataset regardless of how large your model is. We'll show how to [fine tune](/docs/guides/transformers/fine_tuning/) models on your data in the next step.