Update README.md

README.md

@@ -20,36 +20,39 @@ configs:
 # Dataset for the paper: FunQA: Towards Surprising Video Comprehension
 
 Paper: https://huggingface.co/papers/2306.14899.
+Github: https://github.com/Nicous20/FunQA.
+- Supports using [LMMs-Eval](https://github.com/evolvinglmms-lab/lmms-eval) for evaluation.
+- This dataset is also included in the OpenGVLab/MVBench benchmark.
 
-## Repository Layout
-
-```text
-data/
-  train.parquet
-  validation.parquet
-  test.parquet
-  mcqa_test.parquet
-videos/
-  train/
-    train_humor/
-    train_magic/
-    train_creative/
-  validation/
-    val_humor/
-    val_magic/
-    val_creative/
-  test/
-    test_humor/
-    test_magic/
-    test_creative/
-raw/
-  FunQA_train.json
-  FunQA_val.json
-  FunQA_test.json
-  Funqa_mcqa_v1.json
-  train.zip
-  val.zip
-  test.zip
+## Load from Hugging Face Hub
+
+```python
+from datasets import load_dataset
+
+train_ds = load_dataset("fesvhtr/FunQA", "standard", split="train")
+val_ds = load_dataset("fesvhtr/FunQA", "standard", split="validation")
+test_ds = load_dataset("fesvhtr/FunQA", "standard", split="test")
+mcqa_ds = load_dataset("fesvhtr/FunQA", "mcqa", split="mcqa_test")
+```
+
+## Load from Local Files
+
+```python
+from datasets import load_dataset
+
+standard_ds = load_dataset(
+    "parquet",
+    data_files={
+        "train": "data/train.parquet",
+        "validation": "data/validation.parquet",
+        "test": "data/test.parquet",
+    },
+)
+
+mcqa_ds = load_dataset(
+    "parquet",
+    data_files={"mcqa_test": "data/mcqa_test.parquet"},
+)
 ```
 
 ## Dataset Configs
@@ -82,38 +85,7 @@ Columns:
 - `gt`
 - `id`
 
-## Load from Hugging Face Hub
 
-```python
-from datasets import load_dataset
-
-train_ds = load_dataset("your-name/FunQA", "standard", split="train")
-val_ds = load_dataset("your-name/FunQA", "standard", split="validation")
-test_ds = load_dataset("your-name/FunQA", "standard", split="test")
-mcqa_ds = load_dataset("your-name/FunQA", "mcqa", split="mcqa_test")
-```
-
-`load_dataset(...)` loads and caches the annotation files used by the selected config/split. It does not automatically download unrelated archive files in the repository.
-
-## Load from Local Files
-
-```python
-from datasets import load_dataset
-
-standard_ds = load_dataset(
-    "parquet",
-    data_files={
-        "train": "data/train.parquet",
-        "validation": "data/validation.parquet",
-        "test": "data/test.parquet",
-    },
-)
-
-mcqa_ds = load_dataset(
-    "parquet",
-    data_files={"mcqa_test": "data/mcqa_test.parquet"},
-)
-```
 
 ## Download Video Archives
 
@@ -125,7 +97,7 @@ Download one archive into the Hugging Face cache:
 from huggingface_hub import hf_hub_download
 
 zip_path = hf_hub_download(
-    repo_id="your-name/FunQA",
+    repo_id="fesvhtr/FunQA",
     repo_type="dataset",
     filename="raw/test.zip",
 )
@@ -138,7 +110,7 @@ Download the whole repository snapshot:
 from huggingface_hub import snapshot_download
 
 repo_dir = snapshot_download(
-    repo_id="your-name/FunQA",
+    repo_id="fesvhtr/FunQA",
     repo_type="dataset",
 )
 print(repo_dir)
@@ -150,7 +122,7 @@ If needed, download files to a specific local folder instead of only using the d
 from huggingface_hub import snapshot_download
 
 repo_dir = snapshot_download(
-    repo_id="your-name/FunQA",
+    repo_id="fesvhtr/FunQA",
     repo_type="dataset",
     local_dir="funqa_local",
 )