Hugging Face's logo

Datasets:
gineven
/
GeneralScience-MLLM-22K

Tasks:
Question Answering
Modalities:
Image
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
10K<n<100K
Tags:
science
multiple-choice-qa
multimodal
image-text
education
jsonl
License:
Dataset card Data Studio Files
xet
 Community
GeneralScience-MLLM-22K
File size: 3,170 Bytes
b8b027b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
# Upload Guide

This release is ready up to the pre-upload stage. The local package is:

```text
/home/ps/sjj/project/Memory/dataset/general/general_science_release.tar.gz
```

The folder to upload is:

```text
/home/ps/sjj/project/Memory/dataset/general/general_science_release
```

## 1. Pre-upload Check

Run:

```bash
cd /home/ps/sjj/project/Memory/dataset/general/general_science_release
conda run -n memory python scripts/validate_general_science_release.py
```

Expected result:

```text
validation=ok
```

## 2. Hugging Face Upload

### Option A: Web Upload

1. Create a new dataset repository on Hugging Face.
2. Suggested repo name:

```text
gineven/GeneralScience-MLLM-22K
```

3. Upload the contents of `general_science_release/`.
4. Keep the folder structure unchanged:

```text
README.md
general_science_card.md
stats.json
train.jsonl
test.jsonl
images/scienceqa/*.png
scripts/*.py
```

### Option B: CLI Upload

Install or update the HF hub client if needed:

```bash
pip install -U huggingface_hub
```

Login:

```bash
hf auth login
```

Create the dataset repo:

```bash
# You already created this repository:
# https://huggingface.co/datasets/gineven/GeneralScience-MLLM-22K
```

Upload the folder:

```bash
cd /home/ps/sjj/project/Memory/dataset/general
hf upload gineven/GeneralScience-MLLM-22K general_science_release . \
  --repo-type dataset \
  --exclude "*/__pycache__/*" \
  --exclude "*.pyc"
```

If your installed CLI uses the older command name, use:

```bash
huggingface-cli login
# Repository already exists:
# https://huggingface.co/datasets/gineven/GeneralScience-MLLM-22K
```

Then upload with Python API as shown below.

### Option C: Python API Upload

```python
from huggingface_hub import HfApi

api = HfApi()
repo_id = "gineven/GeneralScience-MLLM-22K"

api.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True)
api.upload_folder(
    repo_id=repo_id,
    repo_type="dataset",
    folder_path="/home/ps/sjj/project/Memory/dataset/general/general_science_release",
    path_in_repo=".",
    ignore_patterns=["*/__pycache__/*", "*.pyc"],
)
```

After upload, the expected download link is:

```text
https://huggingface.co/datasets/gineven/GeneralScience-MLLM-22K
```

## 3. ModelScope Upload

ModelScope upload is similar in spirit: create a dataset repository, then upload the same folder contents. Keep `README.md`, `train.jsonl`, `test.jsonl`, `stats.json`, and `images/scienceqa/` at the repository root.

If using `modelscope` CLI/API, first make sure your account is logged in and then upload the `general_science_release/` directory as a dataset repository.

## 4. Netdisk Fallback

If HF/ModelScope credentials are not available, upload:

```text
general_science_release.tar.gz
```

Then share the netdisk URL together with:

```text
general_science_release/general_science_card.md
general_science_release/stats.json
```

## 5. Important License Check

Before public upload, manually verify the upstream ScienceQA license because the local snapshot used for this build does not include license metadata. The current `README.md` marks the combined license as `other` and records the missing ScienceQA license note.