Any-to-Any
Transformers
Safetensors
PyTorch
NemotronH_Nano_Omni_Reasoning_V3
feature-extraction
nvidia
multimodal
custom_code
Instructions to use nvidia/NVIDIA-NemotronLabs-AI-for-Media-Sports-Tennis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/NVIDIA-NemotronLabs-AI-for-Media-Sports-Tennis with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/NVIDIA-NemotronLabs-AI-for-Media-Sports-Tennis", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download bias.md from nvidia/NVIDIA-NemotronLabs-AI-for-Media-Sports-Tennis: direct link, hf CLI and curl.
- Browser
- Download file 954 Bytes
-
https://huggingface.co/nvidia/NVIDIA-NemotronLabs-AI-for-Media-Sports-Tennis/resolve/main/bias.md
- Command line
-
hf download hf://nvidia/NVIDIA-NemotronLabs-AI-for-Media-Sports-Tennis/bias.md
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curl -L -o bias.md https://huggingface.co/nvidia/NVIDIA-NemotronLabs-AI-for-Media-Sports-Tennis/resolve/main/bias.md
954 Bytes
| Field | Response |
|---|---|
| Participation considerations from adversely impacted groups (protected classes) in model design and testing: | None |
| Bias Metric (If Measured): | Tennis point-level Q&A accuracy on the Test (unseen matches) partition — fully held-out matches (MCQ and open-ended). Specific demographic bias metrics have not been separately measured for this fine-tuned release. |
| Measures taken to mitigate against unwanted bias: | Fine-tuning data was curated from in-house tennis datasets with quality filtering. Custom scripts and distribution analysis are applied across annotation categories and match types to assess potential bias. |