diff --git a/evaluation/tasks/blimp/__init__.py b/evaluation/tasks/blimp/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/evaluation/tasks/blimp/blimp.py b/evaluation/tasks/blimp/blimp.py
new file mode 100644
index 0000000..c06c07c
--- /dev/null
+++ b/evaluation/tasks/blimp/blimp.py
@@ -0,0 +1,67 @@
+from datasets import load_dataset
+from torch.utils.data import Dataset
+from tqdm import tqdm
+
+from evaluation.tasks.auto_task import AutoTask
+
+from .task_names import blimp_task_names
+
+
+class BLIMPDataset(Dataset):
+    def __init__(self):
+        super().__init__()
+
+        self.items = [
+            load_dataset("blimp", task, split="train") for task in blimp_task_names
+        ]
+
+    def __len__(self):
+        return len(self.items)
+
+    def __getitem__(self, index):
+        return self.items[index]
+
+
+class BLIMPTask(AutoTask):
+    @staticmethod
+    def get_display_name() -> str:
+        return "blimp"
+
+    def evaluate(self) -> None:
+        dataset = BLIMPDataset()
+        num_correct = 0
+        num_items = 0
+
+        for task_dataset in dataset:
+            for sample in tqdm(
+                task_dataset,
+                desc=f"Evaluating {self.get_display_name()} - {task_dataset.config_name}",
+            ):
+                tokenized_good = self.tokenizer(
+                    sample["sentence_good"], return_tensors="pt"
+                )["input_ids"]
+                tokenized_bad = self.tokenizer(
+                    sample["sentence_bad"], return_tensors="pt"
+                )["input_ids"]
+
+                logits_good = self.model(
+                    input_ids=tokenized_good.to(self.device),
+                ).logits
+                logits_bad = self.model(
+                    input_ids=tokenized_bad.to(self.device),
+                ).logits
+
+                # Compute sentence log probabilities from full LM probability distribution
+                log_prob_good = logits_good[
+                    0, range(tokenized_good.shape[1] - 1), tokenized_good[0, 1:]
+                ].sum()
+                log_prob_bad = logits_bad[
+                    0, range(tokenized_bad.shape[1] - 1), tokenized_bad[0, 1:]
+                ].sum()
+
+                if log_prob_good > log_prob_bad:
+                    num_correct += 1
+
+                num_items += 1
+
+        self.metrics["accuracy"] = num_correct / num_items
diff --git a/evaluation/tasks/blimp/english.json b/evaluation/tasks/blimp/english.json
new file mode 100644
index 0000000..319b5d8
--- /dev/null
+++ b/evaluation/tasks/blimp/english.json
@@ -0,0 +1,3 @@
+{
+    "target_langs": ["english"]
+}
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diff --git a/evaluation/tasks/blimp/task_names.py b/evaluation/tasks/blimp/task_names.py
new file mode 100644
index 0000000..f4d789b
--- /dev/null
+++ b/evaluation/tasks/blimp/task_names.py
@@ -0,0 +1,69 @@
+blimp_task_names = [
+    "adjunct_island",
+    "anaphor_gender_agreement",
+    "anaphor_number_agreement",
+    "animate_subject_passive",
+    "animate_subject_trans",
+    "causative",
+    "complex_NP_island",
+    "coordinate_structure_constraint_complex_left_branch",
+    "coordinate_structure_constraint_object_extraction",
+    "determiner_noun_agreement_1",
+    "determiner_noun_agreement_2",
+    "determiner_noun_agreement_irregular_1",
+    "determiner_noun_agreement_irregular_2",
+    "determiner_noun_agreement_with_adj_2",
+    "determiner_noun_agreement_with_adj_irregular_1",
+    "determiner_noun_agreement_with_adj_irregular_2",
+    "determiner_noun_agreement_with_adjective_1",
+    "distractor_agreement_relational_noun",
+    "distractor_agreement_relative_clause",
+    "drop_argument",
+    "ellipsis_n_bar_1",
+    "ellipsis_n_bar_2",
+    "existential_there_object_raising",
+    "existential_there_quantifiers_1",
+    "existential_there_quantifiers_2",
+    "existential_there_subject_raising",
+    "expletive_it_object_raising",
+    "inchoative",
+    "intransitive",
+    "irregular_past_participle_adjectives",
+    "irregular_past_participle_verbs",
+    "irregular_plural_subject_verb_agreement_1",
+    "irregular_plural_subject_verb_agreement_2",
+    "left_branch_island_echo_question",
+    "left_branch_island_simple_question",
+    "matrix_question_npi_licensor_present",
+    "npi_present_1",
+    "npi_present_2",
+    "only_npi_licensor_present",
+    "only_npi_scope",
+    "passive_1",
+    "passive_2",
+    "principle_A_c_command",
+    "principle_A_case_1",
+    "principle_A_case_2",
+    "principle_A_domain_1",
+    "principle_A_domain_2",
+    "principle_A_domain_3",
+    "principle_A_reconstruction",
+    "regular_plural_subject_verb_agreement_1",
+    "regular_plural_subject_verb_agreement_2",
+    "sentential_negation_npi_licensor_present",
+    "sentential_negation_npi_scope",
+    "sentential_subject_island",
+    "superlative_quantifiers_1",
+    "superlative_quantifiers_2",
+    "tough_vs_raising_1",
+    "tough_vs_raising_2",
+    "transitive",
+    "wh_island",
+    "wh_questions_object_gap",
+    "wh_questions_subject_gap",
+    "wh_questions_subject_gap_long_distance",
+    "wh_vs_that_no_gap",
+    "wh_vs_that_no_gap_long_distance",
+    "wh_vs_that_with_gap",
+    "wh_vs_that_with_gap_long_distance",
+]
\ No newline at end of file