Update valse_vlms.py

valse_vlms.py

@@ -1,96 +1,97 @@
-import os
-from datasets import DatasetInfo, GeneratorBasedBuilder, SplitGenerator, Version, Features, Value, Sequence, Image, Split
-
-_CITATION = """\
-@inproceedings{parcalabescu-etal-2022-valse,
-    title = "{VALSE}: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena",
-    author = "Parcalabescu, Letitia  and
-      Cafagna, Michele  and
-      Muradjan, Lilitta  and
-      Frank, Anette  and
-      Calixto, Iacer  and
-      Gatt, Albert",
-    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
-    month = may,
-    year = "2022",
-    address = "Dublin, Ireland",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2022.acl-long.567",
-    pages = "8253--8280",
-    abstract = "We propose VALSE (Vision And Language Structured Evaluation), a novel benchmark designed for testing general-purpose pretrained vision and language (V{\&}L) models for their visio-linguistic grounding capabilities on specific linguistic phenomena. VALSE offers a suite of six tests covering various linguistic constructs. Solving these requires models to ground linguistic phenomena in the visual modality, allowing more fine-grained evaluations than hitherto possible. We build VALSE using methods that support the construction of valid foils, and report results from evaluating five widely-used V{\&}L models. Our experiments suggest that current models have considerable difficulty addressing most phenomena. Hence, we expect VALSE to serve as an important benchmark to measure future progress of pretrained V{\&}L models from a linguistic perspective, complementing the canonical task-centred V{\&}L evaluations.",
-}
-"""
-
-_DESCRIPTION = """\
-Code and datasets for "VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena".
-"""
-
-_HOMEPAGE = "https://huggingface.co/datasets/Mayfull/valse_vlms"
-_LICENSE = "MIT License"
-
-class VALSEVLMsDataset(GeneratorBasedBuilder):
-    VERSION = Version("1.0.0")
-
-    def _info(self):
-        return DatasetInfo(
-            description=_DESCRIPTION,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-            features=Features(
-                {
-                    "images": Image(),  # Single image
-                    "positive_caption": Sequence(Value("string")),
-                    "negative_caption": Sequence(Value("string")),
-                    "original_file_name": Value("string"),
-                    "dataset": Value("string"),
-                    "key": Value("string"),
-                    "linguistic_phenomena": Value("string"),
-                    "original_split": Value("string"),
-                }
-            ),
-        )
-
-    def _split_generators(self, dl_manager):
-        # URLs for images.zip and examples.jsonl
-        urls_to_download = {
-            "images": "https://huggingface.co/datasets/Mayfull/valse_vlms/resolve/main/images.zip",
-            "examples": "https://huggingface.co/datasets/Mayfull/valse_vlms/resolve/main/examples.jsonl",
-        }
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            SplitGenerator(
-                name=Split.TEST,
-                gen_kwargs={
-                    "examples_file": downloaded_files["examples"],
-                    "images_dir": downloaded_files["images"],
-                },
-            ),
-        ]
-
-    def _generate_examples(self, examples_file, images_dir):
-        # Read the examples.jsonl file
-        with open(examples_file, "r", encoding="utf-8") as f:
-            for idx, line in enumerate(f):
-                data = eval(line.strip())
-
-                # Get image path
-                image_file_name = data.get("image_file_name")
-                image_path = os.path.join(images_dir, image_file_name)
-
-                # Ensure the image file exists
-                if not os.path.exists(image_path):
-                    continue  # Skip if image not found
-
-                # Prepare the example
-                yield idx, {
-                    "images": image_path,
-                    "positive_caption": data.get("positive_caption", []),
-                    "negative_caption": data.get("negative_caption", []),
-                    "original_file_name": data.get("original_file_name", ""),
-                    "dataset": data.get("dataset", ""),
-                    "key": data.get("key", ""),
-                    "linguistic_phenomena": data.get("linguistic_phenomena", ""),
-                    "original_split": data.get("original_split", ""),
-                }
+import os
+from datasets import DatasetInfo, GeneratorBasedBuilder, SplitGenerator, Version, Features, Value, Sequence, Image, Split
+
+_CITATION = """\
+@inproceedings{parcalabescu-etal-2022-valse,
+    title = "{VALSE}: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena",
+    author = "Parcalabescu, Letitia  and
+      Cafagna, Michele  and
+      Muradjan, Lilitta  and
+      Frank, Anette  and
+      Calixto, Iacer  and
+      Gatt, Albert",
+    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = may,
+    year = "2022",
+    address = "Dublin, Ireland",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.acl-long.567",
+    pages = "8253--8280",
+    abstract = "We propose VALSE (Vision And Language Structured Evaluation), a novel benchmark designed for testing general-purpose pretrained vision and language (V{\&}L) models for their visio-linguistic grounding capabilities on specific linguistic phenomena. VALSE offers a suite of six tests covering various linguistic constructs. Solving these requires models to ground linguistic phenomena in the visual modality, allowing more fine-grained evaluations than hitherto possible. We build VALSE using methods that support the construction of valid foils, and report results from evaluating five widely-used V{\&}L models. Our experiments suggest that current models have considerable difficulty addressing most phenomena. Hence, we expect VALSE to serve as an important benchmark to measure future progress of pretrained V{\&}L models from a linguistic perspective, complementing the canonical task-centred V{\&}L evaluations.",
+}
+"""
+
+_DESCRIPTION = """\
+Code and datasets for "VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena".
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/Mayfull/valse_vlms"
+_LICENSE = "MIT License"
+
+class VALSEVLMsDataset(GeneratorBasedBuilder):
+    VERSION = Version("1.0.0")
+
+    def _info(self):
+        return DatasetInfo(
+            description=_DESCRIPTION,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            features=Features(
+                {
+                    "images": Sequence(Image()),  # List of images
+                    "positive_caption": Sequence(Value("string")),
+                    "negative_caption": Sequence(Value("string")),
+                    "original_file_name": Value("string"),
+                    "dataset": Value("string"),
+                    "key": Value("string"),
+                    "linguistic_phenomena": Value("string"),
+                    "original_split": Value("string"),
+                }
+            ),
+        )
+
+    def _split_generators(self, dl_manager):
+        # URLs for images.zip and examples.jsonl
+        urls_to_download = {
+            "images": "https://huggingface.co/datasets/Mayfull/valse_vlms/resolve/main/images.zip",
+            "examples": "https://huggingface.co/datasets/Mayfull/valse_vlms/resolve/main/examples.jsonl",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            SplitGenerator(
+                name=Split.TEST,
+                gen_kwargs={
+                    "examples_file": downloaded_files["examples"],
+                    "images_dir": downloaded_files["images"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, examples_file, images_dir):
+        # Read the examples.jsonl file
+        with open(examples_file, "r", encoding="utf-8") as f:
+            for idx, line in enumerate(f):
+                data = eval(line.strip())
+
+                # Get image path and wrap it in a list
+                image_file_name = data.get("image_file_name")
+                image_path = os.path.join(images_dir, image_file_name)
+                images = [image_path]  # Wrap single image path in a list
+
+                # Ensure the image file exists
+                if not os.path.exists(image_path):
+                    continue  # Skip if image not found
+
+                # Prepare the example
+                yield idx, {
+                    "images": images,  # List of images
+                    "positive_caption": data.get("positive_caption", []),
+                    "negative_caption": data.get("negative_caption", []),
+                    "original_file_name": data.get("original_file_name", ""),
+                    "dataset": data.get("dataset", ""),
+                    "key": data.get("key", ""),
+                    "linguistic_phenomena": data.get("linguistic_phenomena", ""),
+                    "original_split": data.get("original_split", ""),
+                }