Visual Question Answering
Transformers
Safetensors
English
idefics2
text-classification
text-generation-inference
Instructions to use TIGER-Lab/VideoScore with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TIGER-Lab/VideoScore with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="TIGER-Lab/VideoScore")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("TIGER-Lab/VideoScore") model = AutoModelForSequenceClassification.from_pretrained("TIGER-Lab/VideoScore") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -177,7 +177,7 @@ print(aspect_scores)
|
|
| 177 |
model output on visual quality, temporal consistency, dynamic degree,
|
| 178 |
text-to-video alignment, factual consistency, respectively
|
| 179 |
|
| 180 |
-
[2.297, 2.
|
| 181 |
"""
|
| 182 |
|
| 183 |
```
|
|
|
|
| 177 |
model output on visual quality, temporal consistency, dynamic degree,
|
| 178 |
text-to-video alignment, factual consistency, respectively
|
| 179 |
|
| 180 |
+
[2.297, 2.469, 2.906, 2.766, 2.516]
|
| 181 |
"""
|
| 182 |
|
| 183 |
```
|