From 6de92e00861da14df41859f98bdcb00d4a8fd71d Mon Sep 17 00:00:00 2001
From: stephantul <stephantul@gmail.com>
Date: Tue, 4 Aug 2026 19:35:43 +0200
Subject: [PATCH 1/4] feat: refactor inference

---
 model2vec/inference/README.md     |    2 +-
 model2vec/inference/__init__.py   |   10 +-
 model2vec/inference/evaluation.py |  113 +++
 model2vec/inference/mlp.py        |   79 ++
 model2vec/inference/model.py      |  206 ++--
 model2vec/train/README.md         |   14 +-
 model2vec/train/base.py           |    2 +-
 model2vec/train/classifier.py     |   24 +-
 model2vec/train/utils.py          |   89 +-
 pyproject.toml                    |    4 +-
 tests/test_inference.py           |   26 +-
 uv.lock                           | 1490 +++++++++++++++++++++++++++--
 12 files changed, 1786 insertions(+), 273 deletions(-)
 create mode 100644 model2vec/inference/evaluation.py
 create mode 100644 model2vec/inference/mlp.py

diff --git a/model2vec/inference/README.md b/model2vec/inference/README.md
index a14c3e1d..deff5802 100644
--- a/model2vec/inference/README.md
+++ b/model2vec/inference/README.md
@@ -1,6 +1,6 @@
 # Inference
 
-This subpackage mainly contains helper functions for inference with trained models that have been exported to `scikit-learn` compatible pipelines.
+This subpackage mainly contains helper functions for inference with trained classifier/projector heads, persisted as a `safetensors` file and `config.json` metadata.
 
 If you're looking for information on how to train a model, see [here](../train/README.md).
 
diff --git a/model2vec/inference/__init__.py b/model2vec/inference/__init__.py
index e2499442..99e521db 100644
--- a/model2vec/inference/__init__.py
+++ b/model2vec/inference/__init__.py
@@ -1,10 +1,4 @@
-from model2vec.utils import get_package_extras, importable
-
-_REQUIRED_EXTRA = "inference"
-
-for extra_dependency in get_package_extras("model2vec", _REQUIRED_EXTRA):
-    importable(extra_dependency, _REQUIRED_EXTRA)
-
-from model2vec.inference.model import StaticModelPipeline, evaluate_single_or_multi_label
+from model2vec.inference.evaluation import evaluate_single_or_multi_label
+from model2vec.inference.model import StaticModelPipeline
 
 __all__ = ["StaticModelPipeline", "evaluate_single_or_multi_label"]
diff --git a/model2vec/inference/evaluation.py b/model2vec/inference/evaluation.py
new file mode 100644
index 00000000..df1dcc64
--- /dev/null
+++ b/model2vec/inference/evaluation.py
@@ -0,0 +1,113 @@
+from __future__ import annotations
+
+from collections.abc import Iterable, Sequence
+from typing import Any, cast
+
+import numpy as np
+
+
+def _is_multi_label_shaped(y: list[int] | list[str] | list[list[int]] | list[list[str]]) -> bool:
+    """Check if the labels are in a multi-label shape."""
+    return isinstance(y, (list, tuple)) and len(y) > 0 and isinstance(y[0], (list, tuple, set))
+
+
+def _one_hot(labels: Sequence[Any], classes: Sequence[Any]) -> np.ndarray:
+    """One-hot encode a flat sequence of labels against a fixed set of classes."""
+    index = {label: position for position, label in enumerate(classes)}
+    encoded = np.zeros((len(labels), len(classes)), dtype=int)
+    for row, label in enumerate(labels):
+        encoded[row, index[label]] = 1
+    return encoded
+
+
+def _multi_hot(label_lists: Iterable[Iterable[Any]], classes: Sequence[Any]) -> np.ndarray:
+    """Multi-hot encode a sequence of label lists against a fixed set of classes."""
+    index = {label: position for position, label in enumerate(classes)}
+    label_lists = list(label_lists)
+    encoded = np.zeros((len(label_lists), len(classes)), dtype=int)
+    for row, labels in enumerate(label_lists):
+        for label in labels:
+            encoded[row, index[label]] = 1
+    return encoded
+
+
+def _precision_recall_f1_support(
+    y_true: np.ndarray, y_pred: np.ndarray
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Compute per-class precision, recall, f1, and support from one-hot / multi-hot encoded labels."""
+    true_positive = ((y_true == 1) & (y_pred == 1)).sum(axis=0).astype(float)
+    false_positive = ((y_true == 0) & (y_pred == 1)).sum(axis=0).astype(float)
+    false_negative = ((y_true == 1) & (y_pred == 0)).sum(axis=0).astype(float)
+    support = y_true.sum(axis=0)
+
+    predicted_positive = true_positive + false_positive
+    actual_positive = true_positive + false_negative
+    precision = np.divide(
+        true_positive, predicted_positive, out=np.zeros_like(true_positive), where=predicted_positive > 0
+    )
+    recall = np.divide(true_positive, actual_positive, out=np.zeros_like(true_positive), where=actual_positive > 0)
+    precision_plus_recall = precision + recall
+    f1 = np.divide(
+        2 * precision * recall, precision_plus_recall, out=np.zeros_like(precision), where=precision_plus_recall > 0
+    )
+
+    return precision, recall, f1, support
+
+
+def evaluate_single_or_multi_label(
+    predictions: np.ndarray,
+    y: list[int] | list[str] | list[list[int]] | list[list[str]],
+) -> dict[str, dict[str, float]]:
+    """Evaluate the classifier on a given dataset using a classification report.
+
+    This function computes per-class precision, recall and f1-score (via one-vs-rest / multi-hot encoding), plus
+    overall accuracy, macro average, and weighted average.
+
+    :param predictions: The predictions.
+    :param y: The ground truth labels.
+    :return: A classification report, as a dictionary.
+    """
+    if _is_multi_label_shaped(y):
+        y = cast(list[list[str]] | list[list[int]], y)
+        predictions = cast(np.ndarray, predictions)
+        y_labels = {label for labels in y for label in labels}
+        predicted_labels = {label for labels in predictions for label in labels}
+        classes = sorted(y_labels | predicted_labels)
+        y_transformed = _multi_hot(y, classes)
+        predictions_transformed = _multi_hot(predictions, classes)
+    else:
+        y = cast(list[str] | list[int], y)
+        classes = sorted(set(y) | set(predictions.tolist()))
+        y_transformed = _one_hot(y, classes)
+        predictions_transformed = _one_hot(predictions.tolist(), classes)
+
+    target_names = [str(c) for c in classes]
+    precision, recall, f1, support = _precision_recall_f1_support(y_transformed, predictions_transformed)
+    total_support = float(support.sum())
+    accuracy = float(np.all(y_transformed == predictions_transformed, axis=1).mean())
+
+    report: dict[str, Any] = {
+        name: {
+            "precision": float(precision[idx]),
+            "recall": float(recall[idx]),
+            "f1-score": float(f1[idx]),
+            "support": float(support[idx]),
+        }
+        for idx, name in enumerate(target_names)
+    }
+    report["accuracy"] = accuracy
+    report["macro avg"] = {
+        "precision": float(precision.mean()),
+        "recall": float(recall.mean()),
+        "f1-score": float(f1.mean()),
+        "support": total_support,
+    }
+    weights = support / total_support if total_support > 0 else np.zeros_like(support, dtype=float)
+    report["weighted avg"] = {
+        "precision": float((precision * weights).sum()),
+        "recall": float((recall * weights).sum()),
+        "f1-score": float((f1 * weights).sum()),
+        "support": total_support,
+    }
+
+    return report
diff --git a/model2vec/inference/mlp.py b/model2vec/inference/mlp.py
new file mode 100644
index 00000000..a1bcd519
--- /dev/null
+++ b/model2vec/inference/mlp.py
@@ -0,0 +1,79 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+
+import numpy as np
+
+
+class Activation(str, Enum):
+    SOFTMAX = "softmax"
+    SIGMOID = "sigmoid"
+    IDENTITY = "identity"
+
+
+def _softmax(x: np.ndarray) -> np.ndarray:
+    """Numerically stable softmax over the last axis."""
+    shifted = x - x.max(axis=-1, keepdims=True)
+    exponentiated = np.exp(shifted)
+    return exponentiated / exponentiated.sum(axis=-1, keepdims=True)
+
+
+def _sigmoid(x: np.ndarray) -> np.ndarray:
+    """Numerically stable sigmoid."""
+    return np.where(x >= 0, 1 / (1 + np.exp(-x)), np.exp(x) / (1 + np.exp(x)))
+
+
+@dataclass
+class Layer:
+    weight: np.ndarray
+    bias: np.ndarray
+
+    def __call__(self, x: np.ndarray) -> np.ndarray:
+        """Apply the linear transformation."""
+        return x @ self.weight.T + self.bias
+
+
+class MLPHead:
+    def __init__(
+        self,
+        layers: list[Layer],
+        activation: Activation,
+        classes: np.ndarray | None = None,
+    ) -> None:
+        """An MLP with ReLU activation.
+
+        :param layers: The linear layers, in order.
+        :param activation: The output activation.
+        :param classes: The classes, if the task is a classification task.
+        """
+        self.layers = layers
+        self.activation = activation
+        self.classes_ = classes
+
+    def _logits(self, X: np.ndarray) -> np.ndarray:
+        """Run the forward through the layers."""
+        out = X
+        *hidden_layers, last_layer = self.layers
+        for layer in hidden_layers:
+            out = np.maximum(layer(out), 0.0)
+        return last_layer(out)
+
+    def predict_proba(self, X: np.ndarray) -> np.ndarray:
+        """Predict probabilities, applying the output activation to the raw logits."""
+        logits = self._logits(X)
+        match self.activation:
+            case Activation.SOFTMAX:
+                return _softmax(logits)
+            case Activation.SIGMOID:
+                return _sigmoid(logits)
+            case Activation.IDENTITY:
+                return logits
+
+    def predict_index(self, X: np.ndarray) -> np.ndarray:
+        """Predict the index of the most likely class."""
+        return self._logits(X).argmax(axis=1)
+
+    def predict_regression(self, X: np.ndarray) -> np.ndarray:
+        """Predict the raw (identity-activation) output, e.g. for a projector head."""
+        return self._logits(X)
diff --git a/model2vec/inference/model.py b/model2vec/inference/model.py
index 5a56dd97..342df9fc 100644
--- a/model2vec/inference/model.py
+++ b/model2vec/inference/model.py
@@ -1,56 +1,34 @@
 from __future__ import annotations
 
-import re
 from collections.abc import Sequence
-from enum import Enum
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import TypeVar, cast
+from typing import Any, TypeVar, cast
 
 import huggingface_hub
 import numpy as np
-import skops.io
-from sklearn.metrics import classification_report
-from sklearn.neural_network import MLPClassifier, MLPRegressor
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import MultiLabelBinarizer
+from safetensors.numpy import load_file, save_file
 
+from model2vec.inference.evaluation import evaluate_single_or_multi_label
+from model2vec.inference.mlp import Activation, Layer, MLPHead
 from model2vec.model import PathLike, StaticModel
-from model2vec.modelcards import create_model_card
+from model2vec.persistence import save_pretrained
 
-_DEFAULT_TRUST_PATTERN = re.compile(r"sklearn\..+")
-_DEFAULT_MODEL_FILENAME = "pipeline.skops"
+_DEFAULT_HEAD_FILENAME = "head.safetensors"
 
 LabelType = TypeVar("LabelType", list[str], list[list[str]])
 
 
-class HeadType(str, Enum):
-    CLASSIFIER = "classifier"
-    PROJECTOR = "projector"
-    MULTILABEL = "multilabel"
-
-
 class StaticModelPipeline:
-    def __init__(self, model: StaticModel, head: Pipeline) -> None:
+    def __init__(self, model: StaticModel, head: MLPHead) -> None:
         """Create a pipeline with a StaticModel encoder."""
         self.model = model
         self.head = head
-
-        last_head = self.head[-1]
-        self.classes_: None | np.ndarray = None
-        if isinstance(last_head, MLPRegressor):
-            self.classifier_type = HeadType.PROJECTOR
-        elif isinstance(last_head, MLPClassifier):
-            activation = last_head.out_activation_
-            self.classifier_type = HeadType.MULTILABEL if activation == "logistic" else HeadType.CLASSIFIER
-            self.classes_ = self.head.classes_
-        else:
-            # Default to classifier: the assumption is the user is unlikely to use multilabel here.
-            self.classifier_type = HeadType.CLASSIFIER
+        self.classes_ = head.classes_
 
     @classmethod
     def from_pretrained(
-        cls: type[StaticModelPipeline], path: PathLike, token: str | None = None, trust_remote_code: bool = False
+        cls: type[StaticModelPipeline], path: PathLike, token: str | None = None
     ) -> StaticModelPipeline:
         """Load a StaticModel from a local path or huggingface hub path.
 
@@ -58,17 +36,16 @@ def from_pretrained(
 
         :param path: The path to the folder containing the pipeline, or a repository on the Hugging Face Hub
         :param token: The token to use to download the pipeline from the hub.
-        :param trust_remote_code: Whether to trust the remote code. If this is False, we will only load components coming from `sklearn`.
         :return: The loaded pipeline.
         """
-        model, head = _load_pipeline(path, token, trust_remote_code)
+        model, head = _load_pipeline(path, token)
         model.embedding = np.nan_to_num(model.embedding)
 
         return cls(model, head)
 
     def save_pretrained(self, path: str) -> None:
         """Save the model to a folder."""
-        save_pipeline(self, path)
+        _save_pipeline(self, path)
 
     def push_to_hub(
         self, repo_id: str, subfolder: str | None = None, token: str | None = None, private: bool = False
@@ -83,8 +60,7 @@ def push_to_hub(
         from model2vec.persistence import push_folder_to_hub
 
         with TemporaryDirectory() as temp_dir:
-            save_pipeline(self, temp_dir)
-            self.model.save_pretrained(temp_dir)
+            _save_pipeline(self, temp_dir)
             push_folder_to_hub(Path(temp_dir), subfolder, repo_id, private, token)
 
     def _encode_and_coerce_to_2d(
@@ -140,15 +116,17 @@ def predict(
             multiprocessing_threshold=multiprocessing_threshold,
         )
 
-        if self.classifier_type == HeadType.MULTILABEL:
+        if self.head.activation == Activation.IDENTITY:
+            return self.head.predict_regression(encoded)
+
+        if self.head.activation == Activation.SIGMOID:
             assert self.classes_ is not None
-            out_labels = []
             proba = self.head.predict_proba(encoded)
-            for vector in proba:
-                out_labels.append(self.classes_[vector > threshold])
+            out_labels = [self.classes_[vector > threshold] for vector in proba]
             return np.asarray(out_labels, dtype=object)
 
-        return self.head.predict(encoded)
+        assert self.classes_ is not None
+        return self.classes_[self.head.predict_index(encoded)]
 
     def predict_proba(
         self,
@@ -170,7 +148,7 @@ def predict_proba(
         :return: The predicted labels or probabilities.
         :raises ValueError: If the classifier type is projector.
         """
-        if self.classifier_type == HeadType.PROJECTOR:
+        if self.head.activation == Activation.IDENTITY:
             raise ValueError("You are using evaluate on a projector model. This is not supported.")
         encoded = self._encode_and_coerce_to_2d(
             X,
@@ -184,34 +162,29 @@ def predict_proba(
         return self.head.predict_proba(encoded)
 
     def evaluate(
-        self, X: Sequence[str], y: LabelType, batch_size: int = 1024, threshold: float = 0.5, output_dict: bool = False
-    ) -> str | dict[str, dict[str, float]]:
-        """Evaluate the classifier on a given dataset using scikit-learn's classification report.
+        self, X: Sequence[str], y: LabelType, batch_size: int = 1024, threshold: float = 0.5
+    ) -> dict[str, dict[str, float]]:
+        """Evaluate the classifier on a given dataset using a classification report.
 
         :param X: The texts to predict on.
         :param y: The ground truth labels.
         :param batch_size: The batch size.
         :param threshold: The threshold for multilabel classification.
-        :param output_dict: Whether to output the classification report as a dictionary.
-        :return: A classification report.
+        :return: A classification report, as a dictionary.
         :raises ValueError: If the classifier type is projector.
         """
-        if self.classifier_type == HeadType.PROJECTOR:
+        if self.head.activation == Activation.IDENTITY:
             raise ValueError("You are using evaluate on a projector model. This is not supported.")
         predictions = self.predict(X, show_progress_bar=True, batch_size=batch_size, threshold=threshold)
-        report = evaluate_single_or_multi_label(predictions=predictions, y=y, output_dict=output_dict)
-
-        return report
+        return evaluate_single_or_multi_label(predictions=predictions, y=y)
 
 
-def _load_pipeline(
-    folder_or_repo_path: PathLike, token: str | None = None, trust_remote_code: bool = False
-) -> tuple[StaticModel, Pipeline]:
-    """Load a model and an sklearn pipeline.
+def _load_pipeline(folder_or_repo_path: PathLike, token: str | None = None) -> tuple[StaticModel, MLPHead]:
+    """Load a model and its head.
 
     This assumes the following files are present in the repo:
-    - `pipeline.skops`: The head of the pipeline.
-    - `config.json`: The configuration of the model.
+    - `head.safetensors`: The weights of the head.
+    - `config.json`: The configuration of the model, including the head's metadata.
     - `model.safetensors`: The weights of the model.
     - `tokenizer.json`: The tokenizer of the model.
 
@@ -219,40 +192,40 @@ def _load_pipeline(
     :param token: The token to use to download the pipeline from the hub. If this is None, you will only
         be able to load the pipeline from a local folder, public repository, or a repository that you have access to
         because you are logged in.
-    :param trust_remote_code: Whether to trust the remote code. If this is False,
-        we will only load components coming from `sklearn`. If this is True, we will load all components.
-        If you set this to True, you are responsible for whatever happens.
     :return: The encoder model and the loaded head
-    :raises FileNotFoundError: If the pipeline file does not exist in the folder.
-    :raises ValueError: If an untrusted type is found in the pipeline, and `trust_remote_code` is False.
+    :raises FileNotFoundError: If the head file does not exist in the folder.
     """
     folder_or_repo_path = Path(folder_or_repo_path)
-    model_filename = _DEFAULT_MODEL_FILENAME
-    head_pipeline_path: str | Path
+    head_path: str | Path
     if folder_or_repo_path.exists():
-        head_pipeline_path = folder_or_repo_path / model_filename
-        if not head_pipeline_path.exists():
-            raise FileNotFoundError(f"Pipeline file does not exist in {folder_or_repo_path}")
+        head_path = folder_or_repo_path / _DEFAULT_HEAD_FILENAME
+        if not head_path.exists():
+            raise FileNotFoundError(f"Head file does not exist in {folder_or_repo_path}")
     else:
-        head_pipeline_path = huggingface_hub.hf_hub_download(
-            folder_or_repo_path.as_posix(), model_filename, token=token
-        )
+        head_path = huggingface_hub.hf_hub_download(folder_or_repo_path.as_posix(), _DEFAULT_HEAD_FILENAME, token=token)
 
     model = StaticModel.from_pretrained(folder_or_repo_path)
 
-    unknown_types = skops.io.get_untrusted_types(file=head_pipeline_path)
-    # If the user does not trust remote code, we should check that the unknown types are trusted.
-    # By default, we trust everything coming from scikit-learn.
-    if not trust_remote_code:
-        for t in unknown_types:
-            if not _DEFAULT_TRUST_PATTERN.match(t):
-                raise ValueError(f"Untrusted type {t}.")
-    head = skops.io.load(head_pipeline_path, trusted=unknown_types)
+    head_config = cast(dict[str, Any], model.config.get("head_config", {}))
+    activation = Activation(head_config.get("activation", Activation.IDENTITY.value))
+    n_layers = head_config.get("n_layers", 0)
+    classes = head_config.get("classes")
+
+    tensors = load_file(head_path)
+    layers = [
+        Layer(weight=tensors[f"head.{index}.weight"], bias=tensors[f"head.{index}.bias"]) for index in range(n_layers)
+    ]
+
+    head = MLPHead(
+        layers=layers,
+        activation=activation,
+        classes=np.asarray(classes) if classes is not None else None,
+    )
 
     return model, head
 
 
-def save_pipeline(pipeline: StaticModelPipeline, folder_path: str | Path) -> None:
+def _save_pipeline(pipeline: StaticModelPipeline, folder_path: str | Path) -> None:
     """Save a pipeline to a folder.
 
     :param pipeline: The pipeline to save.
@@ -260,61 +233,38 @@ def save_pipeline(pipeline: StaticModelPipeline, folder_path: str | Path) -> Non
     """
     folder_path = Path(folder_path)
     folder_path.mkdir(parents=True, exist_ok=True)
-    model_filename = _DEFAULT_MODEL_FILENAME
-    head_pipeline_path = folder_path / model_filename
-    skops.io.dump(pipeline.head, head_pipeline_path)
-    pipeline.model.save_pretrained(folder_path)
-    base_model_name = pipeline.model.base_model_name
+
+    head = pipeline.head
+    tensors: dict[str, np.ndarray] = {}
+    for index, layer in enumerate(head.layers):
+        tensors[f"head.{index}.weight"] = layer.weight
+        tensors[f"head.{index}.bias"] = layer.bias
+    save_file(tensors, folder_path / _DEFAULT_HEAD_FILENAME)
+
+    model = pipeline.model
+    config = dict(model.config)
+    config["head_config"] = {
+        "n_layers": len(head.layers),
+        "activation": head.activation.value,
+        "classes": np.asarray(pipeline.classes_).tolist() if pipeline.classes_ is not None else None,
+    }
+
+    base_model_name = model.base_model_name
     if isinstance(base_model_name, list) and base_model_name:
         name = base_model_name[0]
     elif isinstance(base_model_name, str):
         name = base_model_name
     else:
         name = "unknown"
-    create_model_card(
-        folder_path,
+
+    save_pretrained(
+        folder_path=folder_path,
+        embeddings=model.embedding,
+        tokenizer=model.tokenizer,
+        config=config,
         base_model_name=name,
-        language=pipeline.model.language,
+        language=model.language,
+        weights=model.weights,
+        mapping=model.token_mapping,
         template_path="classifier_template.md",
     )
-
-
-def _is_multi_label_shaped(y: list[int] | list[str] | list[list[int]] | list[list[str]]) -> bool:
-    """Check if the labels are in a multi-label shape."""
-    return isinstance(y, (list, tuple)) and len(y) > 0 and isinstance(y[0], (list, tuple, set))
-
-
-def evaluate_single_or_multi_label(
-    predictions: np.ndarray,
-    y: list[int] | list[str] | list[list[int]] | list[list[str]],
-    output_dict: bool = False,
-) -> str | dict[str, dict[str, float]]:
-    """Evaluate the classifier on a given dataset using scikit-learn's classification report.
-
-    :param predictions: The predictions.
-    :param y: The ground truth labels.
-    :param output_dict: Whether to output the classification report as a dictionary.
-    :return: A classification report.
-    """
-    if _is_multi_label_shaped(y):
-        # Cast because the type checker doesn't understand that y is a list of lists.
-        y = cast(list[list[str]] | list[list[int]], y)
-        classes = sorted(set([label for labels in y for label in labels]))
-        mlb = MultiLabelBinarizer(classes=classes)
-        y_transformed = mlb.fit_transform(y)
-        predictions_transformed = mlb.transform(predictions)
-    else:
-        if all(isinstance(label, (str, int)) for label in y):
-            y = cast(list[str] | list[int], y)
-            classes = sorted(set(y))
-        y_transformed = np.array(y)
-        predictions_transformed = np.array(predictions)
-
-    report = classification_report(
-        y_transformed,
-        predictions_transformed,
-        output_dict=output_dict,
-        zero_division=0,  # type: ignore  # incorrect type in sklearn.
-    )
-
-    return report
diff --git a/model2vec/train/README.md b/model2vec/train/README.md
index e6399c5b..e5aab128 100644
--- a/model2vec/train/README.md
+++ b/model2vec/train/README.md
@@ -1,6 +1,6 @@
 # Training
 
-Aside from [distillation](../../README.md#distillation), `model2vec` also supports training simple classifiers on top of static models, using [pytorch](https://pytorch.org/) and [scikit-learn](https://scikit-learn.org/stable/index.html).
+Aside from [distillation](../../README.md#distillation), `model2vec` also supports training simple classifiers on top of static models, using [pytorch](https://pytorch.org/).
 
 We support both single and multi-label classification, which work seamlessly based on the labels you provide.
 
@@ -91,28 +91,22 @@ classifier.fit(ds["train"]["text"], ds["train"]["labels"])
 Then, we can evaluate the classifier:
 
 ```python
-from sklearn import metrics
-from sklearn.preprocessing import MultiLabelBinarizer
-
 classification_report = classifier.evaluate(ds["test"]["text"], ds["test"]["labels"], threshold=0.3)
 print(classification_report)
-# Accuracy: 0.410
-# Precision: 0.527
-# Recall: 0.410
-# F1: 0.439
+# {'accuracy': 0.41, 'macro avg': {'precision': 0.527, 'recall': 0.41, 'f1-score': 0.439, ...}, ...}
 ```
 
 The scores are competitive with the popular [roberta-base-go_emotions](https://huggingface.co/SamLowe/roberta-base-go_emotions) model, while our model is orders of magnitude faster.
 
 # Persistence
 
-You can turn a classifier into a scikit-learn compatible pipeline, as follows:
+You can turn a classifier into a lightweight inference pipeline, as follows:
 
 ```python
 pipeline = classifier.to_pipeline()
 ```
 
-This pipeline object can be persisted using standard pickle-based methods, such as [joblib](https://joblib.readthedocs.io/en/stable/). This makes it easy to use your model in inferene pipelines (no installing torch!), although `joblib` and `pickle` should not be used to share models outside of your organization.
+This strips away `torch`: the head's weights are plain `numpy` arrays, so the resulting `StaticModelPipeline` can be used for inference without installing `torch`.
 
 If you want to persist your pipeline to the Hugging Face hub, you can use our built-in functions:
 
diff --git a/model2vec/train/base.py b/model2vec/train/base.py
index e27da01c..16a032c2 100644
--- a/model2vec/train/base.py
+++ b/model2vec/train/base.py
@@ -266,7 +266,7 @@ def to_static_model(self) -> StaticModel:
         )
 
     def to_pipeline(self) -> StaticModelPipeline:
-        """Convert the model to an sklearn pipeline."""
+        """Convert the model to an inference pipeline."""
         return to_pipeline(self)
 
     def _determine_batch_size(self, batch_size: int | None, train_length: int) -> int:
diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py
index e771c415..7190fc1f 100644
--- a/model2vec/train/classifier.py
+++ b/model2vec/train/classifier.py
@@ -7,7 +7,6 @@
 
 import numpy as np
 import torch
-from sklearn.metrics import jaccard_score
 from tokenizers import Tokenizer
 from torch import nn
 from tqdm import trange
@@ -27,10 +26,18 @@ def _classifier_metrics(head_out: torch.Tensor, y: torch.Tensor, loss: torch.Ten
     return {"val_loss": loss.item(), "val_accuracy": accuracy.item()}
 
 
+def _compute_accuracy(y_true: torch.Tensor, y_pred: torch.Tensor) -> float:
+    """Compute the multilabel accuracy score, averaged over samples."""
+    intersection = (y_true * y_pred).sum(dim=1)
+    union = ((y_true + y_pred) > 0).float().sum(dim=1)
+    scores = torch.where(union > 0, intersection / union, torch.zeros_like(union))
+    return scores.mean().item()
+
+
 def _multilabel_classifier_metrics(head_out: torch.Tensor, y: torch.Tensor, loss: torch.Tensor) -> dict[str, float]:
     """Validation metrics for multi-label classification: loss and Jaccard accuracy."""
     preds = (torch.sigmoid(head_out) > 0.5).float()
-    accuracy = cast(float, jaccard_score(y.cpu(), preds.cpu(), average="samples"))
+    accuracy = _compute_accuracy(y, preds)
     return {"val_loss": loss.item(), "val_accuracy": accuracy}
 
 
@@ -233,22 +240,19 @@ def _determine_class_weight(
         return torch.tensor(weights, dtype=torch.float32)
 
     def evaluate(
-        self, X: list[str], y: LabelType, batch_size: int = 1024, threshold: float = 0.5, output_dict: bool = False
-    ) -> str | dict[str, dict[str, float]]:
-        """Evaluate the classifier on a given dataset using scikit-learn's classification report.
+        self, X: list[str], y: LabelType, batch_size: int = 1024, threshold: float = 0.5
+    ) -> dict[str, dict[str, float]]:
+        """Evaluate the classifier on a given dataset.
 
         :param X: The texts to predict on.
         :param y: The ground truth labels.
         :param batch_size: The batch size.
         :param threshold: The threshold for multilabel classification.
-        :param output_dict: Whether to output the classification report as a dictionary.
-        :return: A classification report.
+        :return: A classification report, as a dictionary.
         """
         self.eval()
         predictions = self.predict(X, show_progress_bar=True, batch_size=batch_size, threshold=threshold)
-        report = evaluate_single_or_multi_label(predictions=predictions, y=y, output_dict=output_dict)
-
-        return report
+        return evaluate_single_or_multi_label(predictions=predictions, y=y)
 
     def _initialize_on_labels(self, y: LabelType) -> None:
         """Sets the output dimensionality, the classes, and initializes the head.
diff --git a/model2vec/train/utils.py b/model2vec/train/utils.py
index d9c115f3..e8877282 100644
--- a/model2vec/train/utils.py
+++ b/model2vec/train/utils.py
@@ -2,18 +2,16 @@
 
 import logging
 import random
-from collections import Counter
-from typing import TYPE_CHECKING
+from collections import Counter, defaultdict
+from typing import TYPE_CHECKING, Any
 
 import numpy as np
 import torch
-from sklearn.model_selection import train_test_split as sklearn_split
-from sklearn.neural_network import MLPClassifier, MLPRegressor
-from sklearn.pipeline import make_pipeline
 from tokenizers import Tokenizer
 from torch import nn
 
 from model2vec.inference import StaticModelPipeline
+from model2vec.inference.mlp import Activation, Layer, MLPHead
 
 if TYPE_CHECKING:
     from model2vec.train.base import BaseFinetuneable
@@ -41,34 +39,34 @@ def get_probable_pad_token_id(tokenizer: Tokenizer) -> int:
 
 
 def to_pipeline(model: "BaseFinetuneable | StaticModelForClassification") -> StaticModelPipeline:
-    """Convert the model to an sklearn pipeline."""
+    """Convert the model to an inference pipeline."""
     from model2vec.train.classifier import StaticModelForClassification
 
     static_model = model.to_static_model()
 
-    random_state = np.random.RandomState(42)
-    n_items = model.out_dim
-    X = random_state.randn(n_items, static_model.dim)
-    y: np.ndarray | list[str]
+    layers = [
+        Layer(weight=module.weight.detach().cpu().numpy(), bias=module.bias.detach().cpu().numpy())
+        for module in model.head
+        if isinstance(module, nn.Linear)
+    ]
+
+    classes: np.ndarray | None = None
     if isinstance(model, StaticModelForClassification):
-        y = model.classes_
-        mlp_head = MLPClassifier(hidden_layer_sizes=(model.hidden_dim,) * model.n_layers)
-        activation = "logistic" if model.multilabel else "softmax"
+        classes = np.asarray(model.classes_)
+        activation = Activation.SIGMOID if model.multilabel else Activation.SOFTMAX
     else:
-        y = random_state.randn(n_items, n_items)
-        mlp_head = MLPRegressor(hidden_layer_sizes=(model.hidden_dim,) * model.n_layers)
-        activation = "identity"
-    mlp_head.fit(X, y)
+        activation = Activation.IDENTITY
+
+    head = MLPHead(layers=layers, activation=activation, classes=classes)
 
-    for index, layer in enumerate([module for module in model.head if isinstance(module, nn.Linear)]):
-        mlp_head.coefs_[index] = layer.weight.detach().cpu().numpy().T
-        mlp_head.intercepts_[index] = layer.bias.detach().cpu().numpy()
+    return StaticModelPipeline(static_model, head)
 
-    mlp_head.n_outputs_ = model.out_dim
-    mlp_head.out_activation_ = activation
-    pipeline = make_pipeline(mlp_head)
 
-    return StaticModelPipeline(static_model, pipeline)
+def _index(sequence: Any, indices: list[int]) -> Any:
+    """Index a sequence with a list of indices, preserving the container type."""
+    if isinstance(sequence, list):
+        return [sequence[index] for index in indices]
+    return sequence[indices]
 
 
 def train_test_split(
@@ -81,15 +79,46 @@ def train_test_split(
     For single-label classification, stratification is attempted (if possible).
     For multilabel classification, a random split is performed.
     """
-    stratify_data = None
-    if isinstance(y, list) and isinstance(y[0], (str, int)):
+    rng = random.Random(DEFAULT_RANDOM_SEED)
+    n = len(X)
+
+    stratify = isinstance(y, list) and len(y) > 0 and isinstance(y[0], (str, int))
+    if stratify:
         label_counts = Counter(y)
         if min(label_counts.values()) < 2:
             logger.info("Some classes have fewer than 2 samples. Stratification is disabled.")
-            stratify_data = None
-        else:
-            stratify_data = y
-    return sklearn_split(X, y, test_size=test_size, random_state=42, shuffle=True, stratify=stratify_data)  # type: ignore
+            stratify = False
+
+    train_indices: list[int]
+    test_indices: list[int]
+    if stratify:
+        indices_by_label: dict[Any, list[int]] = defaultdict(list)
+        for index, label in enumerate(y):
+            indices_by_label[label].append(index)
+
+        train_indices = []
+        test_indices = []
+        for indices in indices_by_label.values():
+            indices = indices[:]
+            rng.shuffle(indices)
+            n_test = min(max(1, round(len(indices) * test_size)), len(indices) - 1)
+            test_indices.extend(indices[:n_test])
+            train_indices.extend(indices[n_test:])
+        rng.shuffle(train_indices)
+        rng.shuffle(test_indices)
+    else:
+        indices = list(range(n))
+        rng.shuffle(indices)
+        n_test = min(max(1, round(n * test_size)), max(n - 1, 0))
+        test_indices = indices[:n_test]
+        train_indices = indices[n_test:]
+
+    X_train = _index(X, train_indices)
+    X_test = _index(X, test_indices)
+    y_train = _index(y, train_indices)
+    y_test = _index(y, test_indices)
+
+    return X_train, X_test, y_train, y_test
 
 
 def seed_everything(seed: int) -> None:
diff --git a/pyproject.toml b/pyproject.toml
index 70f0a1f4..cc6c2fd1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -60,9 +60,7 @@ dev = [
 
 distill = ["torch", "transformers<5.4.0", "scikit-learn", "skeletoken>=0.4.0,<0.5.0"]
 onnx = ["onnx", "torch"]
-# train also installs inference
-train = ["torch", "scikit-learn", "skops"]
-inference = ["scikit-learn", "skops"]
+train = ["torch"]
 quantization = ["scikit-learn"]
 integration = ["mteb"]
 
diff --git a/tests/test_inference.py b/tests/test_inference.py
index fea74ad8..7cbe77cb 100644
--- a/tests/test_inference.py
+++ b/tests/test_inference.py
@@ -1,17 +1,16 @@
 import os
-import re
 from tempfile import TemporaryDirectory
-from unittest.mock import patch
 
 import pytest
 
-from model2vec.inference.model import HeadType, StaticModelPipeline
+from model2vec.inference.mlp import Activation
+from model2vec.inference.model import StaticModelPipeline
 
 
 def test_init_predict(mock_inference_pipeline: StaticModelPipeline) -> None:
     """Test successful init and predict with StaticModelPipeline."""
     target: list[str] | list[list[str]]
-    if mock_inference_pipeline.classifier_type == HeadType.MULTILABEL:
+    if mock_inference_pipeline.head.activation == Activation.SIGMOID:
         assert mock_inference_pipeline.classes_ is not None
         if isinstance(mock_inference_pipeline.classes_[0], str):
             target = [["a", "b"]]
@@ -29,7 +28,7 @@ def test_init_predict(mock_inference_pipeline: StaticModelPipeline) -> None:
 
 def test_init_predict_projector(mock_inference_pipeline_projector: StaticModelPipeline) -> None:
     """Test successful init and predict with StaticModelPipeline."""
-    assert mock_inference_pipeline_projector.classifier_type == HeadType.PROJECTOR
+    assert mock_inference_pipeline_projector.head.activation == Activation.IDENTITY
     assert mock_inference_pipeline_projector.classes_ is None
     with pytest.raises(ValueError):
         mock_inference_pipeline_projector.predict_proba(["dog"])
@@ -49,7 +48,7 @@ def test_init_predict_proba(mock_inference_pipeline: StaticModelPipeline) -> Non
 def test_init_evaluate(mock_inference_pipeline: StaticModelPipeline) -> None:
     """Test successful init and evaluate with StaticModelPipeline."""
     target: list[str] | list[list[str]]
-    if mock_inference_pipeline.classifier_type == HeadType.MULTILABEL:
+    if mock_inference_pipeline.head.activation == Activation.SIGMOID:
         assert mock_inference_pipeline.classes_ is not None
         if isinstance(mock_inference_pipeline.classes_[0], str):
             target = [["a", "b"]]
@@ -70,7 +69,7 @@ def test_roundtrip_save(mock_inference_pipeline: StaticModelPipeline) -> None:
         mock_inference_pipeline.save_pretrained(temp_dir)
         loaded = StaticModelPipeline.from_pretrained(temp_dir)
         target: list[str] | list[list[str]]
-        if mock_inference_pipeline.classifier_type == HeadType.MULTILABEL:
+        if mock_inference_pipeline.head.activation == Activation.SIGMOID:
             assert mock_inference_pipeline.classes_ is not None
             if isinstance(mock_inference_pipeline.classes_[0], str):
                 target = [["a", "b"]]
@@ -88,20 +87,11 @@ def test_roundtrip_save(mock_inference_pipeline: StaticModelPipeline) -> None:
         assert loaded.predict_proba(["dog"]).argmax(1).tolist() == [1]
 
 
-@patch("model2vec.inference.model._DEFAULT_TRUST_PATTERN", re.compile("torch"))
-def test_roundtrip_save_mock_trust_pattern(mock_inference_pipeline: StaticModelPipeline) -> None:
-    """Test saving and loading the pipeline."""
-    with TemporaryDirectory() as temp_dir:
-        mock_inference_pipeline.save_pretrained(temp_dir)
-        with pytest.raises(ValueError):
-            StaticModelPipeline.from_pretrained(temp_dir)
-
-
 def test_roundtrip_save_file_gone(mock_inference_pipeline: StaticModelPipeline) -> None:
     """Test saving and loading the pipeline."""
     with TemporaryDirectory() as temp_dir:
         mock_inference_pipeline.save_pretrained(temp_dir)
-        # Rename the file to abc.pipeline, so that it looks like it was downloaded from the hub
-        os.unlink(os.path.join(temp_dir, "pipeline.skops"))
+        # Remove the head file, so that it looks like it was never saved
+        os.unlink(os.path.join(temp_dir, "head.safetensors"))
         with pytest.raises(FileNotFoundError):
             StaticModelPipeline.from_pretrained(temp_dir)
diff --git a/uv.lock b/uv.lock
index bf59e266..0658fe54 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,9 +2,18 @@ version = 1
 revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version >= '3.13'",
-    "python_full_version == '3.12.*'",
-    "python_full_version == '3.11.*'",
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
     "python_full_version < '3.11'",
 ]
 
@@ -15,6 +24,165 @@ exclude-newer-span = "P3D"
 [options.exclude-newer-package]
 skeletoken = false
 
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.7.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ce/f4/eec0465c2f67b2664688d0240b3212d5196fd89e741df67ddb81f8d35658/aiohappyeyeballs-2.7.1.tar.gz", hash = "sha256:065665c041c42a5938ed220bdcd7230f22527fbec085e1853d2402c8a3615d9d", size = 24757, upload-time = "2026-07-01T17:11:55.501Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/71/43/1947f06babed6b3f1d7f38b0c767f52df66bfb2bc10b468c4a7de9eceff2/aiohappyeyeballs-2.7.1-py3-none-any.whl", hash = "sha256:9243213661e29250eb41368e5daa826fc017156c3b8a11440826b2e3ed376472", size = 15038, upload-time = "2026-07-01T17:11:54.055Z" },
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.14.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohappyeyeballs" },
+    { name = "aiosignal" },
+    { name = "async-timeout", marker = "python_full_version < '3.11'" },
+    { name = "attrs" },
+    { name = "frozenlist" },
+    { name = "multidict" },
+    { name = "propcache" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+    { name = "yarl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/58/d9/22ce5786ac0c1653ae8b6c23bded02c1686d11f0dbb45b31ce128e0df985/aiohttp-3.14.3.tar.gz", hash = "sha256:9491196535a88924a60afd5b5f434b5b203b6cc616250878dbdb223a8f7844bc", size = 7971213, upload-time = "2026-07-23T01:57:27.037Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2d/4d/4a99fb425c5e0cad715eea7bd190aff46f38b959a0a2dadb993705d34b26/aiohttp-3.14.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:eb0495d778817619273c108784292be161a924b9f5ae5cbbc70a2caa6838250b", size = 765848, upload-time = "2026-07-23T01:52:08.217Z" },
+    { url = "https://files.pythonhosted.org/packages/74/e8/43b85dc55b8e950dc644babe762add781319ea881b57b33d2cce12017d12/aiohttp-3.14.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c3c200cf9757edd785051dc699c7ecbec22110dbfcb3fefc7a9f9695eda8ea7a", size = 517476, upload-time = "2026-07-23T01:52:10.846Z" },
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 version = "2026.2.0"
@@ -295,6 +725,11 @@ wheels = [
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+[package.optional-dependencies]
+http = [
+    { name = "aiohttp" },
+]
+
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 name = "h11"
 version = "0.16.0"
@@ -441,7 +876,9 @@ name = "ipython"
 version = "9.10.1"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version == '3.11.*'",
+    "python_full_version == '3.11.*' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
@@ -466,8 +903,15 @@ name = "ipython"
 version = "9.12.0"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version >= '3.13'",
-    "python_full_version == '3.12.*'",
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
@@ -812,10 +1256,8 @@ distill = [
     { name = "torch" },
     { name = "transformers" },
 ]
-inference = [
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-    { name = "skops" },
+integration = [
+    { name = "mteb" },
 ]
 onnx = [
     { name = "onnx" },
@@ -826,9 +1268,6 @@ quantization = [
     { name = "scikit-learn", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
 ]
 train = [
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-    { name = "scikit-learn", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
-    { name = "skops" },
     { name = "torch" },
 ]
 
@@ -837,6 +1276,7 @@ requires-dist = [
     { name = "ipython", marker = "extra == 'dev'" },
     { name = "jinja2" },
     { name = "joblib" },
+    { name = "mteb", marker = "extra == 'integration'" },
     { name = "mypy", marker = "extra == 'dev'" },
     { name = "numpy" },
     { name = "onnx", marker = "extra == 'onnx'" },
@@ -846,13 +1286,9 @@ requires-dist = [
     { name = "ruff", marker = "extra == 'dev'" },
     { name = "safetensors" },
     { name = "scikit-learn", marker = "extra == 'distill'" },
-    { name = "scikit-learn", marker = "extra == 'inference'" },
     { name = "scikit-learn", marker = "extra == 'quantization'" },
-    { name = "scikit-learn", marker = "extra == 'train'" },
     { name = "setuptools", marker = "extra == 'dev'" },
     { name = "skeletoken", marker = "extra == 'distill'", specifier = ">=0.4.0,<0.5.0" },
-    { name = "skops", marker = "extra == 'inference'" },
-    { name = "skops", marker = "extra == 'train'" },
     { name = "tokenizers", specifier = ">=0.20" },
     { name = "torch", marker = "extra == 'distill'" },
     { name = "torch", marker = "extra == 'onnx'" },
@@ -860,7 +1296,7 @@ requires-dist = [
     { name = "tqdm" },
     { name = "transformers", marker = "extra == 'distill'", specifier = "<5.4.0" },
 ]
-provides-extras = ["dev", "distill", "onnx", "train", "inference", "quantization"]
+provides-extras = ["dev", "distill", "onnx", "train", "quantization", "integration"]
 
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 ]
 
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+    { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
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From 233888f3cdecc44da7a735faae0b99c8ff3a7c95 Mon Sep 17 00:00:00 2001
From: stephantul <stephantul@gmail.com>
Date: Tue, 4 Aug 2026 20:59:29 +0200
Subject: [PATCH 2/4] test coverage

---
 tests/test_inference.py | 54 ++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 53 insertions(+), 1 deletion(-)

diff --git a/tests/test_inference.py b/tests/test_inference.py
index 7cbe77cb..3a203ea3 100644
--- a/tests/test_inference.py
+++ b/tests/test_inference.py
@@ -1,10 +1,14 @@
 import os
+from pathlib import Path
 from tempfile import TemporaryDirectory
+from unittest.mock import patch
 
+import numpy as np
 import pytest
 
-from model2vec.inference.mlp import Activation
+from model2vec.inference.mlp import Activation, Layer, MLPHead
 from model2vec.inference.model import StaticModelPipeline
+from model2vec.model import StaticModel
 
 
 def test_init_predict(mock_inference_pipeline: StaticModelPipeline) -> None:
@@ -95,3 +99,51 @@ def test_roundtrip_save_file_gone(mock_inference_pipeline: StaticModelPipeline)
         os.unlink(os.path.join(temp_dir, "head.safetensors"))
         with pytest.raises(FileNotFoundError):
             StaticModelPipeline.from_pretrained(temp_dir)
+
+
+def test_mlp_head_predict_proba_identity() -> None:
+    """Test that predict_proba returns the raw logits for an identity activation."""
+    layer = Layer(weight=np.eye(3), bias=np.zeros(3))
+    head = MLPHead(layers=[layer], activation=Activation.IDENTITY)
+
+    X = np.array([[1.0, -2.0, 3.0]])
+    proba = head.predict_proba(X)
+
+    assert np.allclose(proba, X)
+
+
+def test_load_pipeline_from_hub(mock_inference_pipeline: StaticModelPipeline) -> None:
+    """Test that a repo id that isn't a local path is downloaded from the hub."""
+    with TemporaryDirectory() as temp_dir:
+        mock_inference_pipeline.save_pretrained(temp_dir)
+        downloaded_model = StaticModel.from_pretrained(temp_dir)
+        head_path = os.path.join(temp_dir, "head.safetensors")
+
+        with (
+            patch("model2vec.inference.model.huggingface_hub.hf_hub_download", return_value=head_path) as mock_download,
+            patch("model2vec.inference.model.StaticModel.from_pretrained", return_value=downloaded_model),
+        ):
+            loaded = StaticModelPipeline.from_pretrained("fake/repo-id")
+
+        mock_download.assert_called_once_with("fake/repo-id", "head.safetensors", token=None)
+        assert loaded.predict(["dog"]).tolist() == mock_inference_pipeline.predict(["dog"]).tolist()
+
+
+def test_push_to_hub(mock_inference_pipeline: StaticModelPipeline) -> None:
+    """Test that push_to_hub saves the pipeline to a temp folder before pushing it to the hub."""
+    captured: dict[str, object] = {}
+
+    def _capture_upload(
+        folder_path: Path, subfolder: str | None, repo_id: str, private: bool, token: str | None
+    ) -> None:
+        captured["files"] = sorted(p.name for p in folder_path.iterdir())
+        captured["repo_id"] = repo_id
+        captured["private"] = private
+
+    with patch("model2vec.persistence.push_folder_to_hub", side_effect=_capture_upload) as mock_push:
+        mock_inference_pipeline.push_to_hub("fake/repo-id", private=True)
+
+    mock_push.assert_called_once()
+    assert captured["repo_id"] == "fake/repo-id"
+    assert captured["private"] is True
+    assert "head.safetensors" in captured["files"]  # type: ignore[operator]

From 46cfd3df20c23e3842462d18362a77389d7ad8f1 Mon Sep 17 00:00:00 2001
From: stephantul <stephantul@gmail.com>
Date: Wed, 5 Aug 2026 09:53:20 +0200
Subject: [PATCH 3/4] test coverage

---
 model2vec/inference/README.md   |  13 +++
 model2vec/inference/__init__.py |   4 +-
 model2vec/inference/model.py    | 101 ++++++++++++++++-
 tests/test_inference.py         | 186 +++++++++++++++++++++++++++++++-
 4 files changed, 298 insertions(+), 6 deletions(-)

diff --git a/model2vec/inference/README.md b/model2vec/inference/README.md
index deff5802..74f3c5b4 100644
--- a/model2vec/inference/README.md
+++ b/model2vec/inference/README.md
@@ -16,3 +16,16 @@ label = classifier.predict("Attitudes towards cattle in the Alps: a study in let
 ```
 
 This should just work.
+
+# Migrating a legacy pipeline
+
+Pipelines saved by older versions of model2vec store the head as a `scikit-learn`/`skops` `pipeline.skops` file instead of `head.safetensors`. `from_pretrained` still loads these automatically, falling back to the legacy format and emitting a warning. This requires `scikit-learn` and `skops` to be installed.
+
+To upgrade a pipeline to the current format (and silence the warning), convert it with `convert_legacy_pipeline` and save the result:
+
+```python
+from model2vec.inference import convert_legacy_pipeline
+
+pipeline = convert_legacy_pipeline("path/or/repo-id/of/legacy/pipeline")
+pipeline.save_pretrained("path/to/save")
+```
diff --git a/model2vec/inference/__init__.py b/model2vec/inference/__init__.py
index 99e521db..3b2961e3 100644
--- a/model2vec/inference/__init__.py
+++ b/model2vec/inference/__init__.py
@@ -1,4 +1,4 @@
 from model2vec.inference.evaluation import evaluate_single_or_multi_label
-from model2vec.inference.model import StaticModelPipeline
+from model2vec.inference.model import StaticModelPipeline, convert_legacy_pipeline
 
-__all__ = ["StaticModelPipeline", "evaluate_single_or_multi_label"]
+__all__ = ["StaticModelPipeline", "convert_legacy_pipeline", "evaluate_single_or_multi_label"]
diff --git a/model2vec/inference/model.py b/model2vec/inference/model.py
index 342df9fc..ffd3845b 100644
--- a/model2vec/inference/model.py
+++ b/model2vec/inference/model.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import warnings
 from collections.abc import Sequence
 from pathlib import Path
 from tempfile import TemporaryDirectory
@@ -7,6 +8,7 @@
 
 import huggingface_hub
 import numpy as np
+from huggingface_hub.errors import EntryNotFoundError
 from safetensors.numpy import load_file, save_file
 
 from model2vec.inference.evaluation import evaluate_single_or_multi_label
@@ -15,6 +17,7 @@
 from model2vec.persistence import save_pretrained
 
 _DEFAULT_HEAD_FILENAME = "head.safetensors"
+_LEGACY_HEAD_FILENAME = "pipeline.skops"
 
 LabelType = TypeVar("LabelType", list[str], list[list[str]])
 
@@ -34,6 +37,10 @@ def from_pretrained(
 
         NOTE: if you load a private model from the huggingface hub, you need to pass a token.
 
+        NOTE: if the pipeline was saved by an older version of model2vec (a `pipeline.skops` file instead of
+        `head.safetensors`), this falls back to loading it as a legacy pipeline and emits a warning. This requires
+        `scikit-learn` and `skops` to be installed. See `convert_legacy_pipeline` to upgrade it to the current format.
+
         :param path: The path to the folder containing the pipeline, or a repository on the Hugging Face Hub
         :param token: The token to use to download the pipeline from the hub.
         :return: The loaded pipeline.
@@ -193,16 +200,23 @@ def _load_pipeline(folder_or_repo_path: PathLike, token: str | None = None) -> t
         be able to load the pipeline from a local folder, public repository, or a repository that you have access to
         because you are logged in.
     :return: The encoder model and the loaded head
-    :raises FileNotFoundError: If the head file does not exist in the folder.
+    :raises FileNotFoundError: If neither the head file nor a legacy pipeline file exist in the folder.
     """
     folder_or_repo_path = Path(folder_or_repo_path)
     head_path: str | Path
     if folder_or_repo_path.exists():
         head_path = folder_or_repo_path / _DEFAULT_HEAD_FILENAME
         if not head_path.exists():
-            raise FileNotFoundError(f"Head file does not exist in {folder_or_repo_path}")
+            if not (folder_or_repo_path / _LEGACY_HEAD_FILENAME).exists():
+                raise FileNotFoundError(f"Head file does not exist in {folder_or_repo_path}")
+            return _load_legacy_pipeline(folder_or_repo_path, token)
     else:
-        head_path = huggingface_hub.hf_hub_download(folder_or_repo_path.as_posix(), _DEFAULT_HEAD_FILENAME, token=token)
+        try:
+            head_path = huggingface_hub.hf_hub_download(
+                folder_or_repo_path.as_posix(), _DEFAULT_HEAD_FILENAME, token=token
+            )
+        except EntryNotFoundError:
+            return _load_legacy_pipeline(folder_or_repo_path, token)
 
     model = StaticModel.from_pretrained(folder_or_repo_path)
 
@@ -225,6 +239,87 @@ def _load_pipeline(folder_or_repo_path: PathLike, token: str | None = None) -> t
     return model, head
 
 
+def _load_legacy_pipeline(folder_or_repo_path: PathLike, token: str | None) -> tuple[StaticModel, MLPHead]:
+    """Load a model and its head from a legacy (scikit-learn/skops based) pipeline."""
+    _legacy_warning = (
+        f"No `{_DEFAULT_HEAD_FILENAME}` found for {folder_or_repo_path}; falling back to the legacy "
+        f"`{_LEGACY_HEAD_FILENAME}` format. Save this pipeline with `save_pretrained` to upgrade it."
+    )
+    warnings.warn(_legacy_warning, stacklevel=3)
+    converted = convert_legacy_pipeline(folder_or_repo_path, token=token)
+    return converted.model, converted.head
+
+
+def convert_legacy_pipeline(
+    path: PathLike, token: str | None = None, trust_remote_code: bool = False
+) -> StaticModelPipeline:
+    """Convert an old-style (scikit-learn/skops based) `StaticModelPipeline` to the current, safetensors-based format.
+
+    This requires `scikit-learn` and `skops` to be installed.
+
+    :param path: The path to a local folder, or a repository on the Hugging Face Hub, containing a legacy pipeline
+        saved as a `pipeline.skops` file.
+    :param token: The token to use to download the pipeline from the hub.
+    :param trust_remote_code: Whether to trust the remote code in the skops file. If this is False, we will only
+        load components coming from `sklearn`.
+    :return: A new-style `StaticModelPipeline`, which can be persisted with `save_pretrained` or `push_to_hub`.
+    :raises ImportError: If `scikit-learn` and `skops` are not installed.
+    :raises FileNotFoundError: If the pipeline file does not exist in the folder.
+    :raises ValueError: If an untrusted type is found in the pipeline, or the head is not an MLP with a supported
+        hidden activation.
+    """
+    import re
+
+    try:
+        import skops.io
+        from sklearn.neural_network import MLPClassifier, MLPRegressor
+        from sklearn.pipeline import Pipeline
+    except ImportError as exc:
+        raise ImportError("Converting a legacy pipeline requires `scikit-learn` and `skops`. ") from exc
+
+    folder_or_repo_path = Path(path)
+    legacy_head_path: str | Path
+    if folder_or_repo_path.exists():
+        legacy_head_path = folder_or_repo_path / _LEGACY_HEAD_FILENAME
+        if not legacy_head_path.exists():
+            raise FileNotFoundError(f"Pipeline file does not exist in {folder_or_repo_path}")
+    else:
+        legacy_head_path = huggingface_hub.hf_hub_download(
+            folder_or_repo_path.as_posix(), _LEGACY_HEAD_FILENAME, token=token
+        )
+
+    model = StaticModel.from_pretrained(folder_or_repo_path)
+    model.embedding = np.nan_to_num(model.embedding)
+
+    untrusted_types = skops.io.get_untrusted_types(file=legacy_head_path)
+    if not trust_remote_code:
+        trusted_pattern = re.compile(r"sklearn\..+")
+        for untrusted_type in untrusted_types:
+            if not trusted_pattern.match(untrusted_type):
+                raise ValueError(f"Untrusted type {untrusted_type}.")
+    legacy_pipeline = cast(Pipeline, skops.io.load(legacy_head_path, trusted=untrusted_types))
+    legacy_head = legacy_pipeline[-1]
+
+    if isinstance(legacy_head, MLPRegressor):
+        activation = Activation.IDENTITY
+        classes = None
+    elif isinstance(legacy_head, MLPClassifier):
+        activation = Activation.SIGMOID if legacy_head.out_activation_ == "logistic" else Activation.SOFTMAX
+        classes = np.asarray(legacy_head.classes_)
+    else:
+        raise ValueError(f"Unsupported legacy head type: {type(legacy_head)}. Expected MLPClassifier or MLPRegressor.")
+
+    if legacy_head.activation != "relu":
+        raise ValueError(f"Unsupported hidden activation: {legacy_head.activation}. Only `relu` is supported.")
+
+    layers = [
+        Layer(weight=coef.T, bias=intercept) for coef, intercept in zip(legacy_head.coefs_, legacy_head.intercepts_)
+    ]
+    head = MLPHead(layers=layers, activation=activation, classes=classes)
+
+    return StaticModelPipeline(model, head)
+
+
 def _save_pipeline(pipeline: StaticModelPipeline, folder_path: str | Path) -> None:
     """Save a pipeline to a folder.
 
diff --git a/tests/test_inference.py b/tests/test_inference.py
index 3a203ea3..78e78c46 100644
--- a/tests/test_inference.py
+++ b/tests/test_inference.py
@@ -1,13 +1,19 @@
 import os
+import sys
 from pathlib import Path
 from tempfile import TemporaryDirectory
 from unittest.mock import patch
 
 import numpy as np
 import pytest
+import skops.io
+from huggingface_hub.errors import EntryNotFoundError
+from sklearn.neural_network import MLPClassifier, MLPRegressor
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
 
 from model2vec.inference.mlp import Activation, Layer, MLPHead
-from model2vec.inference.model import StaticModelPipeline
+from model2vec.inference.model import StaticModelPipeline, convert_legacy_pipeline
 from model2vec.model import StaticModel
 
 
@@ -147,3 +153,181 @@ def _capture_upload(
     assert captured["repo_id"] == "fake/repo-id"
     assert captured["private"] is True
     assert "head.safetensors" in captured["files"]  # type: ignore[operator]
+
+
+def _dump_legacy_pipeline(temp_dir: str, model: StaticModel, legacy_pipeline: object) -> None:
+    model.save_pretrained(temp_dir)
+    skops.io.dump(legacy_pipeline, os.path.join(temp_dir, "pipeline.skops"))
+
+
+def test_convert_legacy_pipeline_softmax(mock_static_model: StaticModel) -> None:
+    """Test converting a legacy single-label classifier pipeline."""
+    rng = np.random.RandomState(0)
+    X = rng.randn(30, mock_static_model.dim)
+    y = np.array(["a", "b", "c"] * 10)
+    mlp = MLPClassifier(hidden_layer_sizes=(8,), max_iter=1, random_state=0).fit(X, y)
+    legacy_pipeline = make_pipeline(mlp)
+
+    with TemporaryDirectory() as temp_dir:
+        _dump_legacy_pipeline(temp_dir, mock_static_model, legacy_pipeline)
+        converted = convert_legacy_pipeline(temp_dir)
+
+    assert converted.head.activation == Activation.SOFTMAX
+    encoded = mock_static_model.encode(["dog", "cat"])
+    assert converted.predict(["dog", "cat"]).tolist() == legacy_pipeline.predict(encoded).tolist()
+
+
+def test_convert_legacy_pipeline_sigmoid(mock_static_model: StaticModel) -> None:
+    """Test converting a legacy multilabel classifier pipeline."""
+    rng = np.random.RandomState(0)
+    X = rng.randn(30, mock_static_model.dim)
+    y = rng.randint(0, 2, size=(30, 3))
+    mlp = MLPClassifier(hidden_layer_sizes=(8,), max_iter=1, random_state=0).fit(X, y)
+    legacy_pipeline = make_pipeline(mlp)
+
+    with TemporaryDirectory() as temp_dir:
+        _dump_legacy_pipeline(temp_dir, mock_static_model, legacy_pipeline)
+        converted = convert_legacy_pipeline(temp_dir)
+
+    assert converted.head.activation == Activation.SIGMOID
+    encoded = mock_static_model.encode(["dog", "cat"])
+    assert np.allclose(converted.predict_proba(["dog", "cat"]), legacy_pipeline.predict_proba(encoded))
+
+
+def test_convert_legacy_pipeline_identity(mock_static_model: StaticModel) -> None:
+    """Test converting a legacy regressor (projector) pipeline."""
+    rng = np.random.RandomState(0)
+    X = rng.randn(30, mock_static_model.dim)
+    y = rng.randn(30, 5)
+    mlp = MLPRegressor(hidden_layer_sizes=(8,), max_iter=1, random_state=0).fit(X, y)
+    legacy_pipeline = make_pipeline(mlp)
+
+    with TemporaryDirectory() as temp_dir:
+        _dump_legacy_pipeline(temp_dir, mock_static_model, legacy_pipeline)
+        converted = convert_legacy_pipeline(temp_dir)
+
+    assert converted.head.activation == Activation.IDENTITY
+    assert converted.classes_ is None
+    encoded = mock_static_model.encode(["dog", "cat"])
+    assert np.allclose(converted.predict(["dog", "cat"]), legacy_pipeline.predict(encoded))
+
+
+def test_convert_legacy_pipeline_file_gone(mock_static_model: StaticModel) -> None:
+    """Test that a missing pipeline.skops file raises a clear error."""
+    with TemporaryDirectory() as temp_dir:
+        mock_static_model.save_pretrained(temp_dir)
+        with pytest.raises(FileNotFoundError):
+            convert_legacy_pipeline(temp_dir)
+
+
+def test_convert_legacy_pipeline_missing_dependencies(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Test that a helpful error is raised when scikit-learn/skops aren't installed."""
+    monkeypatch.setitem(sys.modules, "skops", None)
+    monkeypatch.setitem(sys.modules, "skops.io", None)
+
+    with pytest.raises(ImportError, match="scikit-learn"):
+        convert_legacy_pipeline("some/path")
+
+
+def test_from_pretrained_legacy_fallback_local(mock_static_model: StaticModel) -> None:
+    """Test that from_pretrained falls back to a local legacy pipeline.skops file, with a warning."""
+    rng = np.random.RandomState(0)
+    X = rng.randn(30, mock_static_model.dim)
+    y = np.array(["a", "b", "c"] * 10)
+    mlp = MLPClassifier(hidden_layer_sizes=(8,), max_iter=1, random_state=0).fit(X, y)
+    legacy_pipeline = make_pipeline(mlp)
+
+    with TemporaryDirectory() as temp_dir:
+        _dump_legacy_pipeline(temp_dir, mock_static_model, legacy_pipeline)
+        with pytest.warns(UserWarning, match="legacy"):
+            loaded = StaticModelPipeline.from_pretrained(temp_dir)
+
+    assert loaded.head.activation == Activation.SOFTMAX
+    encoded = mock_static_model.encode(["dog", "cat"])
+    assert loaded.predict(["dog", "cat"]).tolist() == legacy_pipeline.predict(encoded).tolist()
+
+
+def test_from_pretrained_legacy_fallback_hub(mock_static_model: StaticModel) -> None:
+    """Test that from_pretrained falls back to a hub-downloaded legacy pipeline, with a warning."""
+    rng = np.random.RandomState(0)
+    X = rng.randn(30, mock_static_model.dim)
+    y = rng.randn(30, 4)
+    mlp = MLPRegressor(hidden_layer_sizes=(8,), max_iter=1, random_state=0).fit(X, y)
+    legacy_pipeline = make_pipeline(mlp)
+
+    with TemporaryDirectory() as temp_dir:
+        mock_static_model.save_pretrained(temp_dir)
+        pipeline_skops_path = os.path.join(temp_dir, "pipeline.skops")
+        skops.io.dump(legacy_pipeline, pipeline_skops_path)
+        downloaded_model = StaticModel.from_pretrained(temp_dir)
+
+        def _fake_download(repo_id: str, filename: str, token: str | None = None) -> str:
+            if filename == "head.safetensors":
+                raise EntryNotFoundError("no head.safetensors")
+            return pipeline_skops_path
+
+        with (
+            patch("model2vec.inference.model.huggingface_hub.hf_hub_download", side_effect=_fake_download),
+            patch("model2vec.inference.model.StaticModel.from_pretrained", return_value=downloaded_model),
+            pytest.warns(UserWarning, match="legacy"),
+        ):
+            loaded = StaticModelPipeline.from_pretrained("fake/repo-id")
+
+    assert loaded.head.activation == Activation.IDENTITY
+    encoded = mock_static_model.encode(["dog", "cat"])
+    assert np.allclose(loaded.predict(["dog", "cat"]), legacy_pipeline.predict(encoded))
+
+
+def test_convert_legacy_pipeline_untrusted_type(mock_static_model: StaticModel) -> None:
+    """Test that an untrusted type in the legacy pipeline is rejected by default."""
+    rng = np.random.RandomState(0)
+    X = rng.randn(10, mock_static_model.dim)
+    y = np.array(["a", "b"] * 5)
+    legacy_pipeline = make_pipeline(MLPClassifier(hidden_layer_sizes=(4,), max_iter=1, random_state=0).fit(X, y))
+
+    with TemporaryDirectory() as temp_dir:
+        _dump_legacy_pipeline(temp_dir, mock_static_model, legacy_pipeline)
+        with patch("skops.io.get_untrusted_types", return_value=["evil.Type"]):
+            with pytest.raises(ValueError, match="Untrusted type"):
+                convert_legacy_pipeline(temp_dir)
+
+
+def test_convert_legacy_pipeline_unsupported_head(mock_static_model: StaticModel) -> None:
+    """Test that a non-MLP legacy head raises a clear error."""
+    legacy_pipeline = make_pipeline(StandardScaler())
+
+    with TemporaryDirectory() as temp_dir:
+        _dump_legacy_pipeline(temp_dir, mock_static_model, legacy_pipeline)
+        with pytest.raises(ValueError, match="Unsupported legacy head type"):
+            convert_legacy_pipeline(temp_dir)
+
+
+def test_convert_legacy_pipeline_unsupported_activation(mock_static_model: StaticModel) -> None:
+    """Test that a non-relu hidden activation raises a clear error."""
+    rng = np.random.RandomState(0)
+    X = rng.randn(10, mock_static_model.dim)
+    y = np.array(["a", "b"] * 5)
+    mlp = MLPClassifier(hidden_layer_sizes=(4,), activation="tanh", max_iter=1, random_state=0).fit(X, y)
+    legacy_pipeline = make_pipeline(mlp)
+
+    with TemporaryDirectory() as temp_dir:
+        _dump_legacy_pipeline(temp_dir, mock_static_model, legacy_pipeline)
+        with pytest.raises(ValueError, match="Unsupported hidden activation"):
+            convert_legacy_pipeline(temp_dir)
+
+
+def test_save_pipeline_base_model_name_variants(mock_static_model: StaticModel) -> None:
+    """Test that _save_pipeline resolves both list- and str-valued base_model_name."""
+    head = MLPHead(layers=[Layer(weight=np.eye(3), bias=np.zeros(3))], activation=Activation.IDENTITY)
+
+    for base_model_name, expected in [(["a/b", "c/d"], "a/b"), ("a/b", "a/b")]:
+        model = StaticModel(
+            vectors=mock_static_model.embedding,
+            tokenizer=mock_static_model.tokenizer,
+            base_model_name=base_model_name,  # type: ignore[arg-type]
+        )
+        pipeline = StaticModelPipeline(model, head)
+        with TemporaryDirectory() as temp_dir:
+            pipeline.save_pretrained(temp_dir)
+            readme = (Path(temp_dir) / "README.md").read_text()
+        assert expected in readme

From f693e2a0cc66d8319ceb6fc6138ddc20ab73a2cf Mon Sep 17 00:00:00 2001
From: stephantul <stephantul@gmail.com>
Date: Wed, 5 Aug 2026 10:05:00 +0200
Subject: [PATCH 4/4] add 'tests' extra so skops and scikit-learn get installed
 in CI

---
 Makefile       |  2 +-
 pyproject.toml |  1 +
 uv.lock        | 40 +++++++++++++++++++++++++++++++++++++++-
 3 files changed, 41 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index c067b773..cc1db1a1 100644
--- a/Makefile
+++ b/Makefile
@@ -8,7 +8,7 @@ install:
 	uv run pre-commit install
 
 install-no-pre-commit:
-	uv pip install ".[dev,distill,inference,train,onnx,quantization,integration]"
+	uv pip install ".[dev,distill,train,onnx,quantization,integration,tests]"
 
 install-base:
 	uv sync --extra dev
diff --git a/pyproject.toml b/pyproject.toml
index cc6c2fd1..5a097813 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -63,6 +63,7 @@ onnx = ["onnx", "torch"]
 train = ["torch"]
 quantization = ["scikit-learn"]
 integration = ["mteb"]
+tests = ["scikit-learn", "skops"]
 
 [project.urls]
 "Homepage" = "https://github.com/MinishLab"
diff --git a/uv.lock b/uv.lock
index 0658fe54..7bb99d93 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1267,6 +1267,11 @@ quantization = [
     { name = "scikit-learn", version = "1.7.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "scikit-learn", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
 ]
+tests = [
+    { name = "scikit-learn", version = "1.7.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "scikit-learn", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "skops" },
+]
 train = [
     { name = "torch" },
 ]
@@ -1287,8 +1292,10 @@ requires-dist = [
     { name = "safetensors" },
     { name = "scikit-learn", marker = "extra == 'distill'" },
     { name = "scikit-learn", marker = "extra == 'quantization'" },
+    { name = "scikit-learn", marker = "extra == 'tests'" },
     { name = "setuptools", marker = "extra == 'dev'" },
     { name = "skeletoken", marker = "extra == 'distill'", specifier = ">=0.4.0,<0.5.0" },
+    { name = "skops", marker = "extra == 'tests'" },
     { name = "tokenizers", specifier = ">=0.20" },
     { name = "torch", marker = "extra == 'distill'" },
     { name = "torch", marker = "extra == 'onnx'" },
@@ -1296,7 +1303,7 @@ requires-dist = [
     { name = "tqdm" },
     { name = "transformers", marker = "extra == 'distill'", specifier = "<5.4.0" },
 ]
-provides-extras = ["dev", "distill", "onnx", "train", "quantization", "integration"]
+provides-extras = ["dev", "distill", "onnx", "train", "quantization", "integration", "tests"]
 
 [[package]]
 name = "mpmath"
@@ -2157,6 +2164,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/5d/19/fd3ef348460c80af7bb4669ea7926651d1f95c23ff2df18b9d24bab4f3fa/pre_commit-4.5.1-py2.py3-none-any.whl", hash = "sha256:3b3afd891e97337708c1674210f8eba659b52a38ea5f822ff142d10786221f77", size = 226437, upload-time = "2025-12-16T21:14:32.409Z" },
 ]
 
+[[package]]
+name = "prettytable"
+version = "3.18.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "wcwidth" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/81/74/ba08d81e668ccfe8658d7520a307e63c19862c08eb4ccb26f356c5239a7a/prettytable-3.18.0.tar.gz", hash = "sha256:439217116152244369caf3d9f1caf2f9fe29b03bd79e88d2928c8e718c95d680", size = 76373, upload-time = "2026-06-22T16:07:50.174Z" }
+wheels = [
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 version = "3.0.52"
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 version = "0.6.3"