Video-Text-to-Text
Transformers
Safetensors
English
soccer_qa_4b
soccer
video-qa
question-answering
vision-language
multimodal
sports-analysis
Instructions to use sportsvision/soccer-qa-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sportsvision/soccer-qa-4b with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sportsvision/soccer-qa-4b", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| from src.utils.cluster import dataset_paths | |
| from src.utils.logging import get_logger | |
| logger = get_logger("Datasets utils") | |
| def get_dataset_paths(datasets: list[str]): | |
| paths = [] | |
| for d in datasets: | |
| try: | |
| path = dataset_paths().get(d) | |
| except Exception: | |
| raise Exception(f"Unknown dataset: {d}") | |
| paths.append(path) | |
| logger.info(f"Datapaths {paths}") | |
| return paths | |