> For the complete documentation index, see [llms.txt](https://docs.nannyml.com/cloud/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.nannyml.com/cloud/v0.20.2/model-monitoring/tutorials/monitoring-a-text-classification-model.md). # Monitoring a text classification model Tutorial explaining how to monitor text classification models with NannyML In this tutorial, we will use nannyML cloud to monitor a sentiment analysis text classification model where the model's goal is to predict the sentiment (Negative, Neutral, Positive) of a review left on Amazon. ## The model and dataset We will use a model trained on a subset of the [Multilingual Amazon Reviews Dataset](https://huggingface.co/datasets/amazon_reviews_multi). The trained model can be found in the [nannyML's hugging face hub](https://huggingface.co/NannyML/amazon-reviews-sentiment-bert-base-uncased-6000-samples). For details of how this model was produced, check out the blog post: [Are your NLP models deteriorating post-deployment? Let’s use unlabelled data to find out](https://huggingface.co/blog/santiviquez/performance-estimation-nlp-nannyml). ## Reference and analysis sets To evaluate the model in production, we have two sets: * **Reference set** - which contains all model inputs along with the model’s predictions and labels. This set establishes a baseline for every metric we want to monitor. Find the reference set: * **Analysis set** - which contains all model inputs extracted from a production set with the model’s prediction, and in this case, labels. The analysis set is where NannyML analyzes/monitors the model’s performance and data drift of the model using the knowledge gained from the reference set. Find the analysis set: ## Monitoring with nannyML Cloud ### Step 1: Add a new model Click the Add model button to create a new model on your nannyML cloud dashboard.

### Step 2: Define the problem type and main metric Each review that we are classifying can be Negative, Positive, or Neutral. For this reason, we will set the problem type as **Multiclass classification.** We will be monitoring the model's F1-score on a weekly basis.

### Step 3: Configure the Reference set Select "Upload via public link".

Use the following public URL to link the Reference dataset:

### Step 4: Define the reference dataset schema 1. Select the column **timestamp** as the Timestamp column 2. Select the column **predicted\_sentiment** as the Prediction column 3. Select the **real\_sentiment** as the Target column 4. Flag the columns **negative\_sentiment\_pred\_proba, neutral\_sentiment\_pred\_proba, positive\_sentiment\_pred\_proba** as Prediction Scores.

### Step 5: Configure the Analysis set Select "Upload via public link".

Use the following public URL to link the Analysis dataset:

### Step 6: Start monitoring