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
|
@@ -140,6 +140,12 @@ aspect_scores = []
|
|
| 140 |
for i in range(num_aspects):
|
| 141 |
aspect_scores.append(round(logits[0, i].item(),ROUND_DIGIT))
|
| 142 |
print(aspect_scores)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 143 |
```
|
| 144 |
|
| 145 |
### Training
|
|
|
|
| 140 |
for i in range(num_aspects):
|
| 141 |
aspect_scores.append(round(logits[0, i].item(),ROUND_DIGIT))
|
| 142 |
print(aspect_scores)
|
| 143 |
+
|
| 144 |
+
"""
|
| 145 |
+
# model output on visual quality, temporal consistency, dynamic degree, text-to-video alignment, factual consistency, respectively
|
| 146 |
+
[2.2969, 2.4375, 2.8281, 2.5, 2.4688]
|
| 147 |
+
"""
|
| 148 |
+
|
| 149 |
```
|
| 150 |
|
| 151 |
### Training
|