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@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+tot2mem-video/train.jsonl filter=lfs diff=lfs merge=lfs -text
+tot2mem-video-contrastive-train/train.jsonl filter=lfs diff=lfs merge=lfs -text

tot2mem-human-diffusion/human_information_retrieval_1.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:177dc04e227f98020a2e8fab26aa309547e3ab32a0ee1c469285c0a0ccb24cbd
+size 2146156354

tot2mem-video-contrastive-train/train.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f736b9763b650ef8b543ac8f16686cfa143f175fcf39b6d8ad42e6846d4f723
+size 335755691

tot2mem-video/train.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:adfdf0e361e791d970f95fef91b788be5304ba88dac8cce6920e731ede8cb7bd
+size 301303224

tot2mem_dataset.py

@@ -0,0 +1,90 @@
+import json
+import datasets
+
+class Tot2mem(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="tot2mem-video", version=datasets.Version("1.0.0")),
+        datasets.BuilderConfig(name="tot2mem-video-contrastive-train", version=datasets.Version("1.0.0")),
+        datasets.BuilderConfig(name="tot2mem-video-contrastive-test", version=datasets.Version("1.0.0")),
+        datasets.BuilderConfig(name="tot2mem-human-diffusion", version=datasets.Version("1.0.0")),
+    ]
+    DEFAULT_CONFIG_NAME = "task1"
+
+    def _info(self):
+        if self.config.name == "tot2mem-video":
+            return datasets.DatasetInfo(
+                features=datasets.Features({
+                    "id": datasets.Value("string"),
+                    "image": datasets.Sequence(datasets.Value("string")),
+                    "conversations": datasets.Sequence(
+                        dataset.Features({
+                            "from": datasets.Value("string"),
+                            "value": datasets.Value("string")
+                        })
+                    )
+                })
+            )
+        
+        elif self.config.name == "tot2mem-video-contrastive-train": 
+            return datasets.DatasetInfo(
+                features=datasets.Features({
+                    "qry": datasets.Value("string"), 
+                    "qry_image_path": datasets.Sequence(datasets.Value("string")), 
+                    "pos_text": datasets.Value("string"),
+                    "pos_image_path": datasets.Value(None),
+                    "neg_text": datasets.Value("string"),
+                    "neg_image_path": datasets.Value(None),
+                })
+            )
+        
+        elif self.config.name == "tot2mem-video-contrastive-test": 
+            return datasets.DatasetInfo(
+                features=datasets.Features({
+                    "id": datasets.Value("string"),
+                    "real_recall": datasets.Value("string"),
+                    "video_desc": datasets.Value("string"),
+                    "image_path_list": datasets.Sequence(datasets.Value("string"))
+                })
+            )
+
+        elif self.config.name == "tot2mem-human-diffusion":
+            return datasets.DatasetInfo(
+                features=datasets.Features({
+                    "id": datasets.Value("int32"), 
+                    "image": datasets.Sequence(datasets.Value("string")),
+                    "conversations": datasets.Sequence(
+                        dataset.Features({
+                            "from": datasets.Value("string"),
+                            "value": datasets.Value("string")
+                        })
+                    )
+                })
+            )
+
+
+
+    def _split_generators(self, dl_manager):
+        base = self.config.name
+        if base == "tot2mem-video":
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"path": f"{base}/train.jsonl"}),
+                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"path": f"{base}/test.jsonl"}),
+            ]
+        elif base == "tot2mem-video-contrastive-train": 
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"path": f"{base}/train.jsonl"})
+            ]
+        elif base == "tot2mem-video-contrastive-test": 
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"path": f"{base}/test.jsonl"})
+            ]
+        elif base == "tot2mem-human-diffusion": 
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"path": f"{base}/test.jsonl"})
+            ]
+
+    def _generate_examples(self, path):
+        with open(path, "r", encoding="utf-8") as f:
+            for i, line in enumerate(f):
+                ex = json.loads(line)
+                yield i, ex