Instructions to use splitner/single-qa-wnut17 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use splitner/single-qa-wnut17 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("splitner/single-qa-wnut17") model = AutoModel.from_pretrained("splitner/single-qa-wnut17", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from splitner/single-qa-wnut17: direct link, hf CLI and curl.
- Browser
- Download file 932 MB
-
https://huggingface.co/splitner/single-qa-wnut17/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://splitner/single-qa-wnut17@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/splitner/single-qa-wnut17/resolve/refs%2Fpr%2F1/optimizer.pt
932 MB
- Xet hash:
- ac69c42ffc6ef588bd5faa384325f905fde4e579267c3bd016c24e3f9ca90e6a
- Size of remote file:
- 932 MB
- SHA256:
- ef85c5ac383fca93491bf606865310709dfa281c8545e0374d755bcbfae56c86
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.