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‎README.md

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During training, model is periodically uploaded to Hugging Face Hub. You will find the model at `https://huggingface.co/<username>/<project_name>`. An example model that was automatically pushed to Hugging Face Hub is [here](https://huggingface.co/santiadavani/imdb_review_sentiement).
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### 6. Inference using fine-tuned model
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Now, that we have fine-tuned model on Hugging Face Hub, we can use [`pgml.transform`](/docs/open-source/pgml/guides/llms/text-classification) to perform real-time predictions as well as batch predictions.
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Now, that we have fine-tuned model on Hugging Face Hub, we can use [`pgml.transform`](/docs/open-source/pgml/api/pgml.transform) to perform real-time predictions as well as batch predictions.
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**Real-time predictions**
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‎pgml-cms/blog/sentiment-analysis-using-express-js-and-postgresml.md

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Sentiment analysis is a valuable tool for understanding the emotional polarity of text. You can determine if the text is positive, negative, or neutral. Common use cases include understanding product reviews, survey questions, and social media posts.
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In this application, we'll be applying sentiment analysis to note taking. Note taking and journaling can be an excellent practice for work efficiency and self improvement. However, if you are like me, it quickly becomes impossible to find and make use of anything I've written down. Notes that are useful must be easy to navigate. With this motivation, let's create a demo that can record notes throughout the day. Each day will have a summary and sentiment score. That way, if I'm looking for that time a few weeks ago when we were frustrated with our old MLOps platform — it will be easy to find.&#x20;
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In this application, we'll be applying sentiment analysis to note taking. Note taking and journaling can be an excellent practice for work efficiency and self improvement. However, if you are like me, it quickly becomes impossible to find and make use of anything I've written down. Notes that are useful must be easy to navigate. With this motivation, let's create a demo that can record notes throughout the day. Each day will have a summary and sentiment score. That way, if I'm looking for that time a few weeks ago when we were frustrated with our old MLOps platform — it will be easy to find.
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We will perform all the Machine Learning heavy lifting with the pgml extension function `pgml.transform()`. This brings Hugging Face Transformers into our data layer.
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This app is composed of three main parts, reading and writing to a database, performing sentiment analysis on entries, and creating a summary.
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We are going to use [postgresql-client](https://www.npmjs.com/package/postgresql-client) to connect to our DB.&#x20;
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We are going to use [postgresql-client](https://www.npmjs.com/package/postgresql-client) to connect to our DB.
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When the application builds we ensure we have two tables, one for notes and one for the the daily summary and sentiment score.
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We also have three endpoints to hit:
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* `app.get(“/", async (req, res, next)` which returns all the notes for that day and the daily summary.&#x20;
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* `app.get(“/", async (req, res, next)` which returns all the notes for that day and the daily summary.
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* `app.post(“/add", async (req, res, next)` which accepts a new note entry and performs a sentiment analysis. We simplify the score by converting it to 1, 0, -1 for positive, neutral, negative and save it in our notes table.
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```postgresql
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### Final Thoughts
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This app is far from complete but does show an easy and scalable way to get started with ML in Express. From here I encourage you to head over to our [docs](https://postgresml.org/docs/) and see what other features could be added.
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This app is far from complete but does show an easy and scalable way to get started with ML in Express. From here I encourage you to head over to our [docs](https://postgresml.org/docs) and see what other features could be added.
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If SQL is not your thing, no worries. Check out or [JS SDK](https://postgresml.org/docs/open-source/korvus) to streamline all our best practices with simple JavaScript.&#x20;
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If SQL is not your thing, no worries. Check out or [JS SDK](https://postgresml.org/docs/open-source/korvus/) to streamline all our best practices with simple JavaScript.
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We love hearing from you — please reach out to us on [Discord ](https://discord.gg/DmyJP3qJ7U)or simply [Contact Us](https://postgresml.org/contact) here if you have any questions or feedback.&#x20;
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We love hearing from you — please reach out to us on [Discord ](https://discord.gg/DmyJP3qJ7U)or simply [Contact Us](https://postgresml.org/contact) here if you have any questions or feedback.

‎pgml-cms/docs/open-source/pgml/README.md

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Common tasks include:
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- [Splitting text - pgml.chunk()](api/pgml.chunk)
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- [Generating embeddings - pgml.embed()](api/pgml.embed)
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- [Generating text - pgml.transform()](guides/llms/text-generation.md)
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- [Generating text - pgml.transform()](api/pgml.transform)
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- [Streaming generated text - pgml.transform_stream()](api/pgml.transform_stream)
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## Open-source LLMs
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PostgresML defines four SQL functions which allow training regression, classification, and clustering models on tabular data:
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| Function | Description |
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|---------------|-------------|
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| Function | Description |
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|---------------|----------------------------------------------------------------------------------------------------------------------------------------------------|
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| [pgml.train()](api/pgml.train) | Train a model on PostgreSQL tables or views using any algorithm from Scikit-learn, with the additional support for XGBoost, LightGBM and Catboost. |
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| [pgml.predict()](api/pgml.predict/) | Run inference on live application data using a model trained with [pgml.train()](pgml.train). |
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| [pgml.deploy()](api/pgml.deploy) | Deploy a specific version of a model trained with pgml.train(), using your own accuracy metrics. |
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| [pgml.load_dataset()](api/pgml.load_dataset) | Load any of the toy datasets from Scikit-learn or any dataset from Hugging Face. |
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| [pgml.predict()](api/pgml.predict/) | Run inference on live application data using a model trained with [pgml.train()](api/pgml.train). |
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| [pgml.deploy()](api/pgml.deploy) | Deploy a specific version of a model trained with pgml.train(), using your own accuracy metrics. |
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| [pgml.load_dataset()](api/pgml.load_dataset) | Load any of the toy datasets from Scikit-learn or any dataset from Hugging Face. |

‎pgml-cms/docs/open-source/pgml/api/pgml.predict/README.md

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The `pgml.predict()` function is the key value proposition of PostgresML. It provides online predictions using the best, automatically deployed model for a project. The API for predictions is very simple and only requires two arguments: the project name and the features used for prediction.
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```postgresql
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select pgml.predict (
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select pgml.predict(
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project_name TEXT,
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features REAL[]
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)

‎pgml-cms/docs/open-source/pgml/guides/supervised-learning/README.md

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The API for predictions is very simple and only requires two arguments: the project name and the features used for prediction.
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```postgresql
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select pgml.predict (
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select pgml.predict(
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‎pgml-cms/docs/open-source/pgml/guides/supervised-learning/clustering.md

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## Algorithms
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All clustering algorithms implemented by PostgresML are online versions. You may use the [pgml.predict](/docs/open-source/pgml/api/pgml.predict "mention")function to cluster novel data points after the clustering model has been trained.
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All clustering algorithms implemented by PostgresML are online versions. You may use the [pgml.predict](/docs/open-source/pgml/api/pgml.predict/ "mention")function to cluster novel data points after the clustering model has been trained.
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| Algorithm | Reference |
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| ---------------------- | ----------------------------------------------------------------------------------------------------------------- |

‎pgml-cms/docs/open-source/pgml/guides/vector-database.md

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### Installing pgvector
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If you're using our [cloud](https://postgresml.org/signup) or our Docker image, your database has _pgvector_ installed already. If you're self-hosting PostgresML, take a look at our [Self-hosting](/docs/open-source/pgml/developers/self-hosting/README) documentation.
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If you're using our [cloud](https://postgresml.org/signup) or our Docker image, your database has _pgvector_ installed already. If you're self-hosting PostgresML, take a look at our [Self-hosting](/docs/open-source/pgml/developers/self-hosting/) documentation.
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### Working with vectors
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ADD COLUMN
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ALTER TABLE usa_house_prices
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ADD COLUMN embedding VECTOR(384);
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WHERE address = '1 Infinite Loop, Cupertino, California';
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{% endtab %}

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