Instructions to use splitner/single-seqtag-wnut17 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use splitner/single-seqtag-wnut17 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("splitner/single-seqtag-wnut17") model = AutoModel.from_pretrained("splitner/single-seqtag-wnut17", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from splitner/single-seqtag-wnut17: direct link, hf CLI and curl.
- Browser
- Download file 466 MB
-
https://huggingface.co/splitner/single-seqtag-wnut17/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://splitner/single-seqtag-wnut17@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/splitner/single-seqtag-wnut17/resolve/refs%2Fpr%2F1/pytorch_model.bin
466 MB
- Xet hash:
- 58e920d77d750248d72f4a31255d736fced6bf79f719a4cfac2a2443532dbd84
- Size of remote file:
- 466 MB
- SHA256:
- c0e48ef2e19581d58bde1fbf0a2dec0ef86c31cb11e34fa48c3fbe67d2f970bb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.