diff --git a/belar/metrics/__init__.py b/belar/metrics/__init__.py
index 6ad1367e0..8f6d9f56c 100644
--- a/belar/metrics/__init__.py
+++ b/belar/metrics/__init__.py
@@ -1,4 +1,5 @@
 from belar.metrics.base import Evaluation, Metric
 from belar.metrics.similarity import *
 from belar.metrics.simple import *
-from belar.metrics.similarity import SBERTScore
\ No newline at end of file
+from belar.metrics.similarity import SBERTScore
+from belar.metrics.factual import EntailmentScore
diff --git a/belar/metrics/factual.py b/belar/metrics/factual.py
new file mode 100644
index 000000000..fc41df3dd
--- /dev/null
+++ b/belar/metrics/factual.py
@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import typing as t
+from dataclasses import dataclass
+
+from belar.metrics import Metric
+from belar.utils import device_check
+
+
+@dataclass
+class EntailmentScore(Metric):
+    """
+    Entailment score using ground truth as premise and generated text as hypothesis.
+    """
+
+    model_name: str = "typeform/distilbert-base-uncased-mnli"
+    batch_size: int = 4
+    device: t.Literal["cpu", "cuda"] = "cpu"
+
+    def __post_init__(self):
+        self.device = device_check(self.device)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+        self.model = AutoModelForSequenceClassification.from_pretrained(self.model_name)
+        self.model.eval()
+        self.model.to(self.device)
+
+        model_config = self.model.config.to_dict()
+        assert model_config.get("id2label") or model_config.get(
+            "label2id"
+        ), "label-id mapping missing"
+        if model_config.get("id2label") is None:
+            self.id2label = {v: k for k, v in model_config.label2id}
+        else:
+            self.id2label = model_config["id2label"]
+
+    @property
+    def name(self):
+        return "Entailment_score"
+
+    @property
+    def is_batchable(self):
+        return True
+
+    def batch_infer(self, inputs: dict):
+        predictions = []
+        input_ids = inputs["input_ids"]
+        label2id = {value.lower(): key for key, value in self.id2label.items()}
+
+        for idx in range(0, len(input_ids), self.batch_size):
+            batch_ids = input_ids[idx : idx + self.batch_size]
+            output = self.model(batch_ids.to(self.device))
+            pred = output.logits.softmax(axis=-1).detach().cpu()
+            predictions.extend(pred[:, label2id["entailment"]].tolist())
+
+        return predictions
+
+    def score(
+        self,
+        ground_truth: t.List[str],
+        generated_text: t.List[str],
+    ):
+        """
+        ground_truth : premis
+        generated_text : hypothesis
+        returns entailement probability score
+        """
+
+        encodings = self.tokenizer(
+            ground_truth, generated_text, truncation=True, return_tensors="pt"
+        )
+
+        score = self.batch_infer(encodings)
+
+        return score
diff --git a/belar/metrics/similarity.py b/belar/metrics/similarity.py
index 377356675..77ffa64cf 100644
--- a/belar/metrics/similarity.py
+++ b/belar/metrics/similarity.py
@@ -10,6 +10,7 @@
 
 SBERT_METRIC = t.Literal["cosine", "euclidean"]
 
+
 @dataclass
 class SBERTScore(Metric):
     similarity_metric: t.Literal[SBERT_METRIC] = "cosine"
@@ -25,19 +26,15 @@ def name(
     ):
         return f"SBERT_{self.similarity_metric}"
 
+    @property
     def is_batchable(self):
         return True
 
     def score(
         self,
-        ground_truth: str | list[str],
-        generated_text: str | list[str],
+        ground_truth: list[str],
+        generated_text: list[str],
     ):
-        if isinstance(ground_truth, str):
-            ground_truth = [ground_truth]
-        if isinstance(generated_text, str):
-            generated_text = [generated_text]
-
         gndtruth_emb = self.model.encode(
             ground_truth, batch_size=self.batch_size, convert_to_numpy=True
         )
diff --git a/belar/utils.py b/belar/utils.py
new file mode 100644
index 000000000..9564cfa66
--- /dev/null
+++ b/belar/utils.py
@@ -0,0 +1,19 @@
+import torch
+import typing as t
+from warnings import warn
+
+DEVICES = ["cpu", "cuda"]
+
+
+def device_check(device: t.Literal[DEVICES]):
+    if device == "cuda":
+        if torch.cuda.is_available():
+            device = torch.device("cuda")
+        else:
+            warn("cuda not available, using cpu")
+    elif device == "cpu":
+        device = torch.device("cpu")
+    else:
+        raise ValueError(f"Invalid device {device}")
+
+    return device