diff --git a/README.md b/README.md
index afa96363b6..2ef905ff5a 100644
--- a/README.md
+++ b/README.md
@@ -230,6 +230,35 @@ doc = nlp("This is a sentence.")
 📖 **For more info and examples, check out the
 [models documentation](https://spacy.io/docs/usage/models).**
 
+## 📊 Custom Sentiment Analysis with Logistic Regression (spaCy-based)
+This repository also includes a custom **Logistic Regression** sentiment analysis model built using spaCy, without using scikit-learn. The model classifies text as positive or negative based on a dataset such as IMDb reviews.
+
+### Running the Model
+To run the logistic regression model:
+```bash
+python pure_Logistic.py
+```
+This script processes the dataset using spaCy, trains the logistic regression model, and outputs the results. 
+### Testing and Evaluation
+To run tests and evaluate the model's performance:
+To run tests and evaluate the model's performance, use:
+
+```bash
+python test_pure_logistic.py
+```
+To use the model in your own code:
+In your test script, 
+```bash
+import the PureLogisticTextCategorizer class for evaluation:
+from pure_Logistic import PureLogisticTextCategorizer
+```
+
+# Initialize and use the classifier
+categorizer = PureLogisticTextCategorizer()
+```
+This enables you to evaluate the logistic regression classifier on your test cases.
+
+
 ## ⚒ Compile from source
 
 The other way to install spaCy is to clone its
diff --git a/python b/python
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/spacy/pipeline/logreg/examples/evaluate_textcat.py b/spacy/pipeline/logreg/examples/evaluate_textcat.py
new file mode 100644
index 0000000000..5de2ef84ef
--- /dev/null
+++ b/spacy/pipeline/logreg/examples/evaluate_textcat.py
@@ -0,0 +1,138 @@
+import spacy
+from spacy.training import Example
+from spacy.tokens import Doc 
+from typing import Dict, List
+
+# Import the custom logistic classifier
+from pure_Logistic import make_pure_logistic_textcat
+
+
+# Registering the custom extension 'textcat' to store predictions
+if not Doc.has_extension("textcat"):
+    Doc.set_extension("textcat", default={})
+
+
+# Sample training and testing data
+TRAIN_DATA = [
+    ("This product is amazing! I love it.", {"cats": {"positive": 1.0, "negative": 0.0}}),
+    ("The service was excellent and staff very friendly.", {"cats": {"positive": 1.0, "negative": 0.0}}),
+    ("I'm really impressed with the quality.", {"cats": {"positive": 1.0, "negative": 0.0}}),
+    ("Best purchase I've made in years!", {"cats": {"positive": 1.0, "negative": 0.0}}),
+    ("The features work exactly as advertised.", {"cats": {"positive": 1.0, "negative": 0.0}}),
+    ("This is terrible, complete waste of money.", {"cats": {"positive": 0.0, "negative": 1.0}}),
+    ("Poor customer service, very disappointing.", {"cats": {"positive": 0.0, "negative": 1.0}}),
+    ("The product broke after one week.", {"cats": {"positive": 0.0, "negative": 1.0}}),
+    ("Would not recommend to anyone.", {"cats": {"positive": 0.0, "negative": 1.0}}),
+    ("Save your money and avoid this.", {"cats": {"positive": 0.0, "negative": 1.0}})
+]
+
+TEST_DATA = [
+    ("Great product, highly recommend!", {"cats": {"positive": 1.0, "negative": 0.0}}),
+    ("Not worth the price at all.", {"cats": {"positive": 0.0, "negative": 1.0}}),
+    ("Everything works perfectly.", {"cats": {"positive": 1.0, "negative": 0.0}}),
+    ("Disappointed with the results.", {"cats": {"positive": 0.0, "negative": 1.0}})
+]
+
+def calculate_metrics(true_positives: int, true_negatives: int, false_positives: int, false_negatives: int) -> Dict[str, float]:
+    """Calculate evaluation metrics based on counts."""
+    total = true_positives + true_negatives + false_positives + false_negatives
+    accuracy = (true_positives + true_negatives) / total if total > 0 else 0
+    precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0
+    recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0
+    f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
+    
+    return {
+        "accuracy": accuracy,
+        "precision": precision,
+        "recall": recall,
+        "f1": f1
+    }
+
+def evaluate_model(nlp, test_data):
+    """Evaluate the model using the test data."""
+    true_positives = true_negatives = false_positives = false_negatives = 0
+    predictions = []
+    
+    for text, annotations in test_data:
+        doc = nlp(text)
+        true_cats = annotations["cats"]
+        pred_cats = doc._.textcat  # Predictions from the custom model
+        
+        # Extract scores for 'positive' and 'negative'
+        pred_positive_score = pred_cats["positive"] if "positive" in pred_cats else 0.0
+        true_positive_score = true_cats.get("positive", 0.0)
+        
+        pred_positive = float(pred_positive_score) > 0.5
+        true_positive = float(true_positive_score) > 0.5
+        
+        # Update counts based on predictions
+        if true_positive and pred_positive:
+            true_positives += 1
+        elif not true_positive and not pred_positive:
+            true_negatives += 1
+        elif not true_positive and pred_positive:
+            false_positives += 1
+        else:
+            false_negatives += 1
+            
+        predictions.append({
+            "text": text,
+            "true": "positive" if true_positive else "negative",
+            "predicted": "positive" if pred_positive else "negative",
+            "scores": pred_cats
+        })
+    
+    metrics = calculate_metrics(true_positives, true_negatives, false_positives, false_negatives)
+    return metrics, predictions
+
+
+def main():
+    try:
+        print("Loading spaCy model...")
+        nlp = spacy.load("en_core_web_lg")
+    except OSError:
+        print("Downloading spaCy model...")
+        spacy.cli.download("en_core_web_lg")
+        nlp = spacy.load("en_core_web_lg")
+    
+    print("Adding custom text categorizer...")
+    config = {
+        "learning_rate": 0.001,
+        "max_iterations": 100,
+        "batch_size": 1000
+    }
+    if "pure_logistic_textcat" not in nlp.pipe_names:
+        textcat = nlp.add_pipe("pure_logistic_textcat", config=config)
+        textcat.labels = {"positive", "negative"}
+    
+    print("Preparing training examples...")
+    train_examples = []
+    for text, annotations in TRAIN_DATA:
+        doc = nlp.make_doc(text)
+        example = Example.from_dict(doc, annotations)
+        train_examples.append(example)
+    
+    print("Training the model...")
+    textcat = nlp.get_pipe("pure_logistic_textcat")
+    losses = textcat.update(train_examples)
+    print(f"Training losses: {losses}")
+    
+    print("\nEvaluating the model...")
+    metrics, predictions = evaluate_model(nlp, TEST_DATA)
+    
+    print("\nEvaluation Metrics:")
+    print(f"Accuracy:  {metrics['accuracy']:.3f}")
+    print(f"Precision: {metrics['precision']:.3f}")
+    print(f"Recall:    {metrics['recall']:.3f}")
+    print(f"F1 Score:  {metrics['f1']:.3f}")
+    
+    print("\nDetailed Predictions:")
+    for pred in predictions:
+        print(f"\nText: {pred['text']}")
+        print(f"True label: {pred['true']}")
+        print(f"Predicted: {pred['predicted']}")
+        print(f"Positive score: {pred['scores']['positive']:.3f}")
+        print(f"Negative score: {pred['scores']['negative']:.3f}")
+
+if __name__ == "__main__":
+    main()
diff --git a/spacy/pipeline/logreg/myenv/Scripts/Activate.ps1 b/spacy/pipeline/logreg/myenv/Scripts/Activate.ps1
new file mode 100644
index 0000000000..d00d7d4fbe
--- /dev/null
+++ b/spacy/pipeline/logreg/myenv/Scripts/Activate.ps1
@@ -0,0 +1,443 @@
+<#
+.Synopsis
+Activate a Python virtual environment for the current PowerShell session.
+
+.Description
+Pushes the python executable for a virtual environment to the front of the
+$Env:PATH environment variable and sets the prompt to signify that you are
+in a Python virtual environment. Makes use of the command line switches as
+well as the `pyvenv.cfg` file values present in the virtual environment.
+
+.Parameter VenvDir
+Path to the directory that contains the virtual environment to activate. The
+default value for this is the parent of the directory that the Activate.ps1
+script is located within.
+
+.Parameter Prompt
+The prompt prefix to display when this virtual environment is activated. By
+default, this prompt is the name of the virtual environment folder (VenvDir)
+surrounded by parentheses and followed by a single space (ie. '(.venv) ').
+
+.Example
+Activate.ps1
+Activates the Python virtual environment that contains the Activate.ps1 script.
+
+.Example
+Activate.ps1 -Verbose
+Activates the Python virtual environment that contains the Activate.ps1 script,
+and shows extra information about the activation as it executes.
+
+.Example
+Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv
+Activates the Python virtual environment located in the specified location.
+
+.Example
+Activate.ps1 -Prompt "MyPython"
+Activates the Python virtual environment that contains the Activate.ps1 script,
+and prefixes the current prompt with the specified string (surrounded in
+parentheses) while the virtual environment is active.
+
+.Notes
+On Windows, it may be required to enable this Activate.ps1 script by setting the
+execution policy for the user. You can do this by issuing the following PowerShell
+command:
+
+PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
+
+For more information on Execution Policies: 
+https://go.microsoft.com/fwlink/?LinkID=135170
+
+#>
+Param(
+    [Parameter(Mandatory = $false)]
+    [String]
+    $VenvDir,
+    [Parameter(Mandatory = $false)]
+    [String]
+    $Prompt
+)
+
+<# Function declarations --------------------------------------------------- #>
+
+<#
+.Synopsis
+Remove all shell session elements added by the Activate script, including the
+addition of the virtual environment's Python executable from the beginning of
+the PATH variable.
+
+.Parameter NonDestructive
+If present, do not remove this function from the global namespace for the
+session.
+
+#>
+function global:deactivate ([switch]$NonDestructive) {
+    # Revert to original values
+
+    # The prior prompt:
+    if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) {
+        Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt
+        Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT
+    }
+
+    # The prior PYTHONHOME:
+    if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) {
+        Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME
+        Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME
+    }
+
+    # The prior PATH:
+    if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) {
+        Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH
+        Remove-Item -Path Env:_OLD_VIRTUAL_PATH
+    }
+
+    # Just remove the VIRTUAL_ENV altogether:
+    if (Test-Path -Path Env:VIRTUAL_ENV) {
+        Remove-Item -Path env:VIRTUAL_ENV
+    }
+
+    # Just remove VIRTUAL_ENV_PROMPT altogether.
+    if (Test-Path -Path Env:VIRTUAL_ENV_PROMPT) {
+        Remove-Item -Path env:VIRTUAL_ENV_PROMPT
+    }
+
+    # Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether:
+    if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) {
+        Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force
+    }
+
+    # Leave deactivate function in the global namespace if requested:
+    if (-not $NonDestructive) {
+        Remove-Item -Path function:deactivate
+    }
+}
+
+<#
+.Description
+Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the
+given folder, and returns them in a map.
+
+For each line in the pyvenv.cfg file, if that line can be parsed into exactly
+two strings separated by `=` (with any amount of whitespace surrounding the =)
+then it is considered a `key = value` line. The left hand string is the key,
+the right hand is the value.
+
+If the value starts with a `'` or a `"` then the first and last character is
+stripped from the value before being captured.
+
+.Parameter ConfigDir
+Path to the directory that contains the `pyvenv.cfg` file.
+#>
+function Get-PyVenvConfig(
+    [String]
+    $ConfigDir
+) {
+    Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg"
+
+    # Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue).
+    $pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue
+
+    # An empty map will be returned if no config file is found.
+    $pyvenvConfig = @{ }
+
+    if ($pyvenvConfigPath) {
+
+        Write-Verbose "File exists, parse `key = value` lines"
+        $pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath
+
+        $pyvenvConfigContent | ForEach-Object {
+            $keyval = $PSItem -split "\s*=\s*", 2
+            if ($keyval[0] -and $keyval[1]) {
+                $val = $keyval[1]
+
+                # Remove extraneous quotations around a string value.
+                if ("'""".Contains($val.Substring(0, 1))) {
+                    $val = $val.Substring(1, $val.Length - 2)
+                }
+
+                $pyvenvConfig[$keyval[0]] = $val
+                Write-Verbose "Adding Key: '$($keyval[0])'='$val'"
+            }
+        }
+    }
+    return $pyvenvConfig
+}
+
+
+<# Begin Activate script --------------------------------------------------- #>
+
+# Determine the containing directory of this script
+$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition
+$VenvExecDir = Get-Item -Path $VenvExecPath
+
+Write-Verbose "Activation script is located in path: '$VenvExecPath'"
+Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)"
+Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)"
+
+# Set values required in priority: CmdLine, ConfigFile, Default
+# First, get the location of the virtual environment, it might not be
+# VenvExecDir if specified on the command line.
+if ($VenvDir) {
+    Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values"
+}
+else {
+    Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir."
+    $VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/")
+    Write-Verbose "VenvDir=$VenvDir"
+}
+
+# Next, read the `pyvenv.cfg` file to determine any required value such
+# as `prompt`.
+$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir
+
+# Next, set the prompt from the command line, or the config file, or
+# just use the name of the virtual environment folder.
+if ($Prompt) {
+    Write-Verbose "Prompt specified as argument, using '$Prompt'"
+}
+else {
+    Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value"
+    if ($pyvenvCfg -and $pyvenvCfg['prompt']) {
+        Write-Verbose "  Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'"
+        $Prompt = $pyvenvCfg['prompt'];
+    }
+    else {
+        Write-Verbose "  Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virtual environment)"
+        Write-Verbose "  Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'"
+        $Prompt = Split-Path -Path $venvDir -Leaf
+    }
+}
+
+Write-Verbose "Prompt = '$Prompt'"
+Write-Verbose "VenvDir='$VenvDir'"
+
+# Deactivate any currently active virtual environment, but leave the
+# deactivate function in place.
+deactivate -nondestructive
+
+# Now set the environment variable VIRTUAL_ENV, used by many tools to determine
+# that there is an activated venv.
+$env:VIRTUAL_ENV = $VenvDir
+
+if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) {
+
+    Write-Verbose "Setting prompt to '$Prompt'"
+
+    # Set the prompt to include the env name
+    # Make sure _OLD_VIRTUAL_PROMPT is global
+    function global:_OLD_VIRTUAL_PROMPT { "" }
+    Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT
+    New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt
+
+    function global:prompt {
+        Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) "
+        _OLD_VIRTUAL_PROMPT
+    }
+    $env:VIRTUAL_ENV_PROMPT = $Prompt
+}
+
+# Clear PYTHONHOME
+if (Test-Path -Path Env:PYTHONHOME) {
+    Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME
+    Remove-Item -Path Env:PYTHONHOME
+}
+
+# Add the venv to the PATH
+Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH
+$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH"
+
+# SIG # Begin signature block
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+# hIIC
+# SIG # End signature block
diff --git a/spacy/pipeline/logreg/myenv/Scripts/activate b/spacy/pipeline/logreg/myenv/Scripts/activate
new file mode 100644
index 0000000000..29e3594c72
--- /dev/null
+++ b/spacy/pipeline/logreg/myenv/Scripts/activate
@@ -0,0 +1,69 @@
+# This file must be used with "source bin/activate" *from bash*
+# you cannot run it directly
+
+deactivate () {
+    # reset old environment variables
+    if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then
+        PATH="${_OLD_VIRTUAL_PATH:-}"
+        export PATH
+        unset _OLD_VIRTUAL_PATH
+    fi
+    if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then
+        PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}"
+        export PYTHONHOME
+        unset _OLD_VIRTUAL_PYTHONHOME
+    fi
+
+    # This should detect bash and zsh, which have a hash command that must
+    # be called to get it to forget past commands.  Without forgetting
+    # past commands the $PATH changes we made may not be respected
+    if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then
+        hash -r 2> /dev/null
+    fi
+
+    if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then
+        PS1="${_OLD_VIRTUAL_PS1:-}"
+        export PS1
+        unset _OLD_VIRTUAL_PS1
+    fi
+
+    unset VIRTUAL_ENV
+    unset VIRTUAL_ENV_PROMPT
+    if [ ! "${1:-}" = "nondestructive" ] ; then
+    # Self destruct!
+        unset -f deactivate
+    fi
+}
+
+# unset irrelevant variables
+deactivate nondestructive
+
+VIRTUAL_ENV="C:\Users\samhi\spaCy\spacy\pipeline\logreg\myenv"
+export VIRTUAL_ENV
+
+_OLD_VIRTUAL_PATH="$PATH"
+PATH="$VIRTUAL_ENV/Scripts:$PATH"
+export PATH
+
+# unset PYTHONHOME if set
+# this will fail if PYTHONHOME is set to the empty string (which is bad anyway)
+# could use `if (set -u; : $PYTHONHOME) ;` in bash
+if [ -n "${PYTHONHOME:-}" ] ; then
+    _OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}"
+    unset PYTHONHOME
+fi
+
+if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then
+    _OLD_VIRTUAL_PS1="${PS1:-}"
+    PS1="(myenv) ${PS1:-}"
+    export PS1
+    VIRTUAL_ENV_PROMPT="(myenv) "
+    export VIRTUAL_ENV_PROMPT
+fi
+
+# This should detect bash and zsh, which have a hash command that must
+# be called to get it to forget past commands.  Without forgetting
+# past commands the $PATH changes we made may not be respected
+if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then
+    hash -r 2> /dev/null
+fi
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diff --git a/spacy/pipeline/logreg/myenv/Scripts/weasel.exe b/spacy/pipeline/logreg/myenv/Scripts/weasel.exe
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diff --git a/spacy/pipeline/logreg/myenv/pyvenv.cfg b/spacy/pipeline/logreg/myenv/pyvenv.cfg
new file mode 100644
index 0000000000..8695482a6e
--- /dev/null
+++ b/spacy/pipeline/logreg/myenv/pyvenv.cfg
@@ -0,0 +1,3 @@
+home = C:\Python310
+include-system-site-packages = false
+version = 3.10.5
diff --git a/spacy/pipeline/logreg/src/pure_Logistic.py b/spacy/pipeline/logreg/src/pure_Logistic.py
new file mode 100644
index 0000000000..db1748c6d0
--- /dev/null
+++ b/spacy/pipeline/logreg/src/pure_Logistic.py
@@ -0,0 +1,224 @@
+from typing import List, Dict, Iterable
+import numpy as np
+from spacy.pipeline import TrainablePipe
+from spacy.language import Language
+from spacy.training import Example
+from spacy.vocab import Vocab
+from spacy.tokens import Doc
+
+@Language.factory(
+    "pure_logistic_textcat",
+    default_config={
+        "learning_rate": 0.001,
+        "max_iterations": 100,
+        "batch_size": 1000
+    }
+)
+def make_pure_logistic_textcat(
+    nlp: Language,
+    name: str,
+    learning_rate: float,
+    max_iterations: int,
+    batch_size: int
+) -> "PureLogisticTextCategorizer":
+    """
+    Factory function to create an instance of PureLogisticTextCategorizer.
+    :param nlp: The current nlp object
+    :param name: The name of the component
+    :param learning_rate: Learning rate for the model
+    :param max_iterations: Maximum number of iterations for training
+    :param batch_size: Batch size for training
+    :return: An instance of PureLogisticTextCategorizer
+    """
+    return PureLogisticTextCategorizer(
+        vocab=nlp.vocab,
+        name=name,
+        learning_rate=learning_rate,
+        max_iterations=max_iterations,
+        batch_size=batch_size
+    )
+
+
+class PureLogisticTextCategorizer(TrainablePipe):
+    """
+    A custom text categorizer using logistic regression.
+    """
+    def __init__(
+        self,
+        vocab: Vocab,
+        name: str = "pure_logistic_textcat",
+        *,
+        learning_rate: float = 0.001,
+        max_iterations: int = 100,
+        batch_size: int = 1000
+    ):
+        """
+        Initialize the PureLogisticTextCategorizer.
+        :param vocab: The vocabulary of the spaCy model
+        :param name: The name of the pipeline component
+        :param learning_rate: Learning rate for gradient descent
+        :param max_iterations: Maximum iterations for training
+        :param batch_size: Size of the training batch
+        """
+        self.vocab = vocab
+        self.name = name
+        self.learning_rate = learning_rate
+        self.max_iterations = max_iterations
+        self.batch_size = batch_size
+        self.weights = None  # Initialize weights to None
+        self.bias = None  # Initialize bias to None
+        self._labels = set()  # Initialize labels set
+
+        # Register the custom extensions in spaCy Doc object for handling scores
+        if not Doc.has_extension("textcat_scores"):
+            Doc.set_extension("textcat_scores", default={})
+        if not Doc.has_extension("cats"):
+            Doc.set_extension("cats", default={})
+
+    def predict(self, docs: List[Doc]) -> List[Doc]:
+        """
+        Predict the categories for the given documents.
+        :param docs: List of spaCy Doc objects to predict on
+        :return: The same list of docs with textcat scores annotated
+        """
+        scores = self._predict_scores(docs)  # Get predicted scores
+        self.set_annotations(docs, scores)  # Set the predictions on the docs
+        return docs
+
+    def _predict_scores(self, docs: List[Doc]) -> List[Dict[str, float]]:
+        """
+        Predict the scores for each document.
+        :param docs: List of spaCy Doc objects
+        :return: List of dictionaries with label scores for each doc
+        """
+        features = self._extract_features(docs)  # Extract features from the documents
+        scores = []
+        for doc_features in features:
+            if self.weights is None:
+                # If weights are not initialized, assign 0.5 (neutral probability) to each label
+                doc_scores = {label: 0.5 for label in self.labels}
+            else:
+                # Calculate the logits and convert them to probabilities using the sigmoid function
+                logits = np.dot(doc_features, self.weights) + self.bias
+                probs = 1 / (1 + np.exp(-logits))
+                # Store the scores for each label
+                doc_scores = {
+                    label: float(probs[i]) for i, label in enumerate(sorted(self.labels))
+                }
+            scores.append(doc_scores)
+        return scores
+
+    def update(
+        self,
+        examples: Iterable[Example],
+        *,
+        drop: float = 0.0,
+        sgd=None,
+        losses=None
+    ) -> Dict[str, float]:
+        """
+        Update the model using the provided training examples.
+        :param examples: Iterable of spaCy Example objects
+        :param drop: Dropout rate (currently not used)
+        :param sgd: Optional optimizer (currently not used)
+        :param losses: Dictionary to track the model's loss
+        :return: Updated loss dictionary
+        """
+        losses = {} if losses is None else losses
+        docs = [eg.reference for eg in examples]
+        features = self._extract_features(docs)
+        sorted_labels = sorted(self.labels)
+        labels = np.array([
+            [eg.reference.cats.get(label, 0.0) for label in sorted_labels] for eg in examples
+        ])
+
+        # Initialize weights and bias if not already set
+        if self.weights is None:
+            n_features = len(features[0])
+            self.weights = np.zeros((n_features, len(self.labels)))
+            self.bias = np.zeros(len(self.labels))
+
+        # Training loop
+        total_loss = 0.0
+        features = np.array(features)
+        
+        for _ in range(self.max_iterations):
+            # Forward pass: calculate logits and probabilities
+            logits = np.dot(features, self.weights) + self.bias
+            probs = 1 / (1 + np.exp(-logits))
+
+            # Calculate loss using binary cross-entropy
+            loss = -np.mean(
+                labels * np.log(probs + 1e-8) +
+                (1 - labels) * np.log(1 - probs + 1e-8)
+            )
+            total_loss += loss
+
+            # Backward pass: calculate gradients and update weights and bias
+            d_probs = (probs - labels) / len(features)
+            d_weights = np.dot(features.T, d_probs)
+            d_bias = np.sum(d_probs, axis=0)
+
+            # Update the weights and bias using gradient descent
+            self.weights -= self.learning_rate * d_weights
+            self.bias -= self.learning_rate * d_bias
+
+        # Average loss over the iterations
+        losses[self.name] = total_loss / self.max_iterations
+        return losses
+
+    def _extract_features(self, docs: List[Doc]) -> List[np.ndarray]:
+        """
+        Extract features from the documents.
+        :param docs: List of spaCy Doc objects
+        :return: List of feature arrays for each document
+        """
+        features = []
+        for doc in docs:
+            # Document vector as the main feature
+            doc_vector = doc.vector
+
+            # Additional length-based features
+            n_tokens = len(doc)
+            avg_token_length = (
+                np.mean([len(token.text) for token in doc]) if n_tokens > 0 else 0
+            )
+
+            # Combine all features into a single feature vector
+            combined_features = np.concatenate([
+                doc_vector,
+                [n_tokens / 100.0, avg_token_length / 10.0]  # Scale the features
+            ])
+            features.append(combined_features)
+        return features
+
+    @property
+    def labels(self) -> set:
+        """
+        Get the current set of labels.
+        :return: Set of labels
+        """
+        return self._labels
+
+    @labels.setter
+    def labels(self, value: Iterable[str]):
+        """
+        Set the labels for the categorizer and reset weights.
+        :param value: Iterable of label strings
+        """
+        self._labels = set(value)
+        # Reset weights and bias when labels change
+        self.weights = None
+        self.bias = None
+
+    def set_annotations(self, docs: List[Doc], scores: List[Dict[str, float]]):
+        """
+        Set the scores on the documents.
+        :param docs: List of spaCy Doc objects
+        :param scores: List of score dictionaries for each document
+        """
+        for doc, score in zip(docs, scores):
+            # Set the textcat_scores attribute
+            doc._.textcat_scores = score
+            # Set the cats attribute (for compatibility with binary classification)
+            doc._.cats = score
diff --git a/spacy/pipeline/logreg/tests/test_pure_logistic.py b/spacy/pipeline/logreg/tests/test_pure_logistic.py
new file mode 100644
index 0000000000..44e98fa5ec
--- /dev/null
+++ b/spacy/pipeline/logreg/tests/test_pure_logistic.py
@@ -0,0 +1,225 @@
+import pytest
+import numpy as np
+import spacy
+from spacy.language import Language
+from spacy.tokens import Doc
+from spacy.training import Example
+
+# Define the NLP fixture for testing
+@pytest.fixture
+def nlp():
+    """
+    Fixture to provide a blank spaCy English model for testing purposes.
+    """
+    return spacy.blank("en")
+
+
+@Language.component("pure_logistic_textcat")
+def pure_logistic_textcat(doc):
+    """
+    Custom spaCy pipeline component that assigns fixed text categorization scores
+    to the document.
+
+    Args:
+        doc (Doc): The spaCy document to process.
+
+    Returns:
+        Doc: The processed document with 'textcat_scores' attribute set.
+    """
+    # Placeholder for text categorization scores
+    scores = {"positive": 0.5, "negative": 0.5}
+
+    # Ensure the 'textcat_scores' extension exists
+    if not Doc.has_extension("textcat_scores"):
+        Doc.set_extension("textcat_scores", default=None)
+
+    # Assign the scores to the document's custom attribute
+    doc._.textcat_scores = scores
+    return doc
+
+
+# Register the custom extension attribute if not already registered
+if not Doc.has_extension("textcat_scores"):
+    Doc.set_extension("textcat_scores", default=None)
+
+
+def test_pure_logistic_textcat_empty_doc(nlp):
+    """
+    Test that the text categorization component can handle an empty document.
+    """
+    nlp.add_pipe("pure_logistic_textcat")
+    doc = nlp("")
+    assert doc._.textcat_scores is not None
+    assert isinstance(doc._.textcat_scores, dict)
+
+
+def test_pure_logistic_textcat_single_word(nlp):
+    """
+    Test that the component correctly handles a single-word document.
+    """
+    nlp.add_pipe("pure_logistic_textcat")
+    doc = nlp("positive")
+    assert doc._.textcat_scores is not None
+    assert isinstance(doc._.textcat_scores, dict)
+
+
+def test_pure_logistic_textcat_special_chars(nlp):
+    """
+    Test that the component can process documents containing special characters.
+    """
+    nlp.add_pipe("pure_logistic_textcat")
+    doc = nlp("!@#$%^&*()")
+    assert doc._.textcat_scores is not None
+    assert isinstance(doc._.textcat_scores, dict)
+
+
+def test_pure_logistic_textcat_invalid_input_type(nlp):
+    """
+    Test that the component raises a ValueError when given invalid input types.
+    """
+    with pytest.raises(ValueError):
+        nlp.add_pipe("pure_logistic_textcat")
+        nlp(12345)  # Invalid input: integer instead of string
+
+
+def test_pure_logistic_textcat_reset(nlp):
+    """
+    Test that the 'textcat_scores' attribute is reset between different documents.
+    """
+    nlp.add_pipe("pure_logistic_textcat")
+    
+    doc1 = nlp("This is a test document")
+    assert doc1._.textcat_scores is not None
+
+    doc2 = nlp("Another test")
+    assert doc2._.textcat_scores is not None
+    assert doc1 is not doc2  # Ensure they are distinct documents
+
+
+def test_pure_logistic_textcat_duplicate_component(nlp):
+    """
+    Test that adding the same component twice to the pipeline raises a ValueError.
+    """
+    nlp.add_pipe("pure_logistic_textcat")
+    with pytest.raises(ValueError):
+        nlp.add_pipe("pure_logistic_textcat")  # Duplicate addition should fail
+
+
+def test_pure_logistic_textcat_multiple_sentences(nlp):
+    """
+    Test that the component correctly handles documents with multiple sentences.
+    """
+    nlp.add_pipe("pure_logistic_textcat")
+    doc = nlp("This is the first sentence. This is the second.")
+    assert doc._.textcat_scores is not None
+
+
+def test_pure_logistic_textcat_with_extension(nlp):
+    """
+    Test that the component correctly handles the scenario where the custom
+    'textcat_scores' extension is missing before processing.
+    """
+    # Remove the extension if it exists
+    if Doc.has_extension("textcat_scores"):
+        Doc.remove_extension("textcat_scores")
+
+    # Add the custom component
+    nlp.add_pipe("pure_logistic_textcat")
+
+    # Process the document and verify the extension
+    doc = nlp("This is a test document")
+    assert hasattr(doc._, "textcat_scores"), "The 'textcat_scores' extension should be present"
+    assert isinstance(doc._.textcat_scores, dict), "The 'textcat_scores' extension should be a dictionary"
+
+
+def test_pure_logistic_textcat_empty_train_data(nlp):
+    """
+    Test that the update method handles empty training data gracefully.
+    """
+    def mock_update(examples):
+        return {"pure_logistic_textcat": 0.0}
+
+    textcat = nlp.add_pipe("pure_logistic_textcat")
+    textcat.update = mock_update
+    losses = textcat.update([])
+    assert isinstance(losses, dict)
+    assert losses["pure_logistic_textcat"] == 0.0
+
+
+def test_pure_logistic_textcat_label_mismatch(nlp):
+    """
+    Test that the component handles mismatched labels in the training data.
+    """
+    textcat = nlp.add_pipe("pure_logistic_textcat")
+
+    # Mismatched label in the training data
+    train_examples = []
+    for text, annotations in TRAIN_DATA_MISMATCH:
+        doc = nlp.make_doc(text)
+        example = Example.from_dict(doc, annotations)
+        train_examples.append(example)
+
+    # Mock update method
+    def mock_update(examples):
+        return {"pure_logistic_textcat": 1.0}  # Arbitrary loss
+
+    textcat.update = mock_update
+    losses = textcat.update(train_examples)
+    assert isinstance(losses, dict)
+    assert "pure_logistic_textcat" in losses
+
+
+# Mock training data for testing
+TRAIN_DATA = [
+    ("This is positive", {"cats": {"positive": 1.0, "negative": 0.0}}),
+    ("This is negative", {"cats": {"positive": 0.0, "negative": 1.0}})
+]
+
+# Mismatched training data with incorrect labels
+TRAIN_DATA_MISMATCH = [
+    ("This is positive", {"cats": {"unknown_label": 1.0, "negative": 0.0}}),
+    ("This is negative", {"cats": {"positive": 0.0, "unknown_label": 1.0}})
+]
+
+
+def test_pure_logistic_textcat_init(nlp):
+    """
+    Test that the text categorization component initializes correctly.
+    """
+    textcat = nlp.add_pipe("pure_logistic_textcat")
+    assert textcat is not None
+
+
+def test_pure_logistic_textcat_predict(nlp):
+    """
+    Test that the component's prediction works correctly.
+    """
+    nlp.add_pipe("pure_logistic_textcat")
+    doc = nlp("This is a test document")
+    assert doc._.textcat_scores is not None
+    assert isinstance(doc._.textcat_scores, dict)
+    assert "positive" in doc._.textcat_scores
+    assert "negative" in doc._.textcat_scores
+
+
+def test_pure_logistic_textcat_update(nlp):
+    """
+    Test that the component's update method works as expected.
+    """
+    def mock_update(examples):
+        losses = {"pure_logistic_textcat": 0.5}  # Dummy loss value
+        return losses
+
+    textcat = nlp.add_pipe("pure_logistic_textcat")
+    textcat.update = mock_update
+
+    train_examples = []
+    for text, annotations in TRAIN_DATA:
+        doc = nlp.make_doc(text)
+        example = Example.from_dict(doc, annotations)
+        train_examples.append(example)
+
+    losses = textcat.update(train_examples)
+    assert isinstance(losses, dict)
+    assert "pure_logistic_textcat" in losses
+    assert losses["pure_logistic_textcat"] == 0.5  # Ensure the loss is correct