diff --git a/openbb_chat/classifiers/abstract_zeroshot_classifier.py b/openbb_chat/classifiers/abstract_zeroshot_classifier.py
new file mode 100644
index 0000000..8492bfe
--- /dev/null
+++ b/openbb_chat/classifiers/abstract_zeroshot_classifier.py
@@ -0,0 +1,91 @@
+from abc import ABC, abstractmethod
+from typing import List, Tuple
+
+import torch
+from rich.progress import track
+from transformers import (
+    AutoModel,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    PreTrainedModel,
+)
+
+
+class AbstractZeroshotClassifier(ABC):
+    """Abstract parent class of zero-shot classifiers.
+
+    It instantiates the necessary transformers models.
+    """
+
+    def __init__(
+        self,
+        model_id: str,
+        use_automodel_for_seq: bool = False,
+        model_kwargs: dict = {},
+        tokenizer_kwargs: dict = {},
+    ):
+        """Init method.
+
+        Args:
+            model_id (`str`):
+                Name of the HF model to use.
+            model_type (`bool`):
+                Whether to use `AutoModelForSequenceClassification` or `AutoModel` (default).
+        """
+        self.tokenizer = AutoTokenizer.from_pretrained(model_id, **tokenizer_kwargs)
+
+        if not use_automodel_for_seq:
+            self.model = AutoModel.from_pretrained(model_id, **model_kwargs)
+        else:
+            self.model = AutoModelForSequenceClassification.from_pretrained(
+                model_id, **model_kwargs
+            )
+        self.model.eval()
+
+    def classify(self, query: str, keys: List[str]) -> Tuple[str, float, int]:
+        """Given a query, the most similar key is returned.
+
+        Args:
+            query (`str`):
+                Short text to classify into one key.
+            keys (`List[str]`):
+                All possible classes, each one a short description.
+
+        Returns:
+            `str`: The most similar key.
+            `float`: The score obtained.
+            `int`: Index of the key.
+        """
+
+        inputs = self.tokenizer(query, return_tensors="pt")
+        target_embed = self._compute_embed(inputs)
+
+        max_cosine_sim = -1  # min. possible cosine similarity
+        most_sim_descr = ""
+        selected_index = -1
+        for idx, descr in track(enumerate(keys), total=len(keys), description="Processing..."):
+            inputs = self.tokenizer(descr, return_tensors="pt")
+            descr_embed = self._compute_embed(inputs)
+
+            cosine_sim = torch.sum(
+                torch.nn.functional.normalize(target_embed)
+                * torch.nn.functional.normalize(descr_embed)
+            )
+            if cosine_sim > max_cosine_sim:
+                most_sim_descr = descr
+                max_cosine_sim = cosine_sim
+                selected_index = idx
+
+        return most_sim_descr, max_cosine_sim, selected_index
+
+    @abstractmethod
+    def _compute_embed(self, inputs: dict) -> torch.Tensor:
+        """Computes the final embedding of the text given the tokenized inputs and the model.
+
+        Args:
+            inputs (`dict`): tokenized inputs to the model.
+
+        Returns:
+            `torch.Tensor`: embedding of the text. Shape (1, model_dim).
+        """
+        raise NotImplementedError
diff --git a/openbb_chat/classifiers/roberta.py b/openbb_chat/classifiers/roberta.py
new file mode 100644
index 0000000..0c88d9c
--- /dev/null
+++ b/openbb_chat/classifiers/roberta.py
@@ -0,0 +1,18 @@
+import torch
+from transformers import PreTrainedModel
+
+from openbb_chat.classifiers.abstract_zeroshot_classifier import (
+    AbstractZeroshotClassifier,
+)
+
+
+class RoBERTaZeroshotClassifier(AbstractZeroshotClassifier):
+    """Zero-shot classifier based on `sentence-transformers`."""
+
+    def __init__(self, model_id: str = "roberta-base", *args, **kwargs):
+        """Override __init__ to set default model_id."""
+        super().__init__(model_id, *args, **kwargs)
+
+    def _compute_embed(self, inputs: dict) -> torch.Tensor:
+        """Override parent method to use RoBERTa pooler output ([CLS] token)."""
+        return self.model(**inputs).pooler_output
diff --git a/openbb_chat/classifiers/stransformer.py b/openbb_chat/classifiers/stransformer.py
new file mode 100644
index 0000000..52f63a4
--- /dev/null
+++ b/openbb_chat/classifiers/stransformer.py
@@ -0,0 +1,38 @@
+import torch
+from transformers import PreTrainedModel
+
+from openbb_chat.classifiers.abstract_zeroshot_classifier import (
+    AbstractZeroshotClassifier,
+)
+
+
+class STransformerZeroshotClassifier(AbstractZeroshotClassifier):
+    """Zero-shot classifier based on `sentence-transformers`."""
+
+    def __init__(self, model_id: str = "sentence-transformers/all-MiniLM-L6-v2", *args, **kwargs):
+        """Override __init__ to set default model_id."""
+        super().__init__(model_id, *args, **kwargs)
+
+    def _compute_embed(self, inputs: dict) -> torch.Tensor:
+        """Override parent method to use `sentence-transformers` models in HF."""
+        return self._mean_pooling(self.model(**inputs), inputs["attention_mask"])
+
+    def _mean_pooling(self, model_output: object, attention_mask: torch.Tensor) -> torch.Tensor:
+        """
+        Mean Pooling - Take attention mask into account for correct averaging.
+
+        Args:
+            model_output (`object`): output of `sentence-transformers` model in HF.
+            attention_mask (`torch.Tensor`): attention mask denoting padding.
+
+        Returns:
+            `torch.Tensor`: final embedding computed from the model output.
+        """
+        # Code from https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
+        token_embeddings = model_output[
+            0
+        ]  # First element of model_output contains all token embeddings
+        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
+            input_mask_expanded.sum(1), min=1e-9
+        )
diff --git a/tests/classifiers/test_roberta.py b/tests/classifiers/test_roberta.py
new file mode 100644
index 0000000..47bfdb2
--- /dev/null
+++ b/tests/classifiers/test_roberta.py
@@ -0,0 +1,21 @@
+from unittest.mock import patch
+
+import pytest
+from transformers import AutoModel
+
+from openbb_chat.classifiers.roberta import RoBERTaZeroshotClassifier
+
+
+@patch("torch.sum")
+@patch.object(AutoModel, "from_pretrained")
+def test_classify(mocked_automodel_frompretrained, mocked_torch_sum):
+    mocked_torch_sum.return_value = 1
+
+    roberta_zeroshot = RoBERTaZeroshotClassifier()
+    key, score, idx = roberta_zeroshot.classify("Here is a dog", ["dog", "cat"])
+
+    mocked_automodel_frompretrained.assert_called_once_with("roberta-base")
+    mocked_torch_sum.assert_called()
+    assert key == "dog"
+    assert score == 1
+    assert idx == 0
diff --git a/tests/classifiers/test_stransformers.py b/tests/classifiers/test_stransformers.py
new file mode 100644
index 0000000..bbd585b
--- /dev/null
+++ b/tests/classifiers/test_stransformers.py
@@ -0,0 +1,33 @@
+from unittest.mock import patch
+
+import pytest
+from transformers import AutoModel, AutoTokenizer
+
+from openbb_chat.classifiers.stransformer import STransformerZeroshotClassifier
+
+
+@patch("torch.clamp")
+@patch("torch.sum")
+@patch.object(AutoTokenizer, "from_pretrained")
+@patch.object(AutoModel, "from_pretrained")
+def test_classify(
+    mocked_automodel_frompretrained,
+    mocked_tokenizer_frompretrained,
+    mocked_torch_sum,
+    mocked_torch_clamp,
+):
+    mocked_torch_sum.return_value = 1
+
+    stransformer_zeroshot = STransformerZeroshotClassifier()
+    key, score, idx = stransformer_zeroshot.classify("Here is a dog", ["dog", "cat"])
+
+    mocked_automodel_frompretrained.assert_called_once_with(
+        "sentence-transformers/all-MiniLM-L6-v2"
+    )
+    mocked_tokenizer_frompretrained.assert_called_once_with(
+        "sentence-transformers/all-MiniLM-L6-v2"
+    )
+    mocked_torch_sum.assert_called()
+    assert key == "dog"
+    assert score == 1
+    assert idx == 0