Instructions to use splitner/single-seqtag-ontonotes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use splitner/single-seqtag-ontonotes with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("splitner/single-seqtag-ontonotes") model = AutoModel.from_pretrained("splitner/single-seqtag-ontonotes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from splitner/single-seqtag-ontonotes: direct link, hf CLI and curl.
- Browser
- Download file 1.05 GB
-
https://huggingface.co/splitner/single-seqtag-ontonotes/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://splitner/single-seqtag-ontonotes@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/splitner/single-seqtag-ontonotes/resolve/refs%2Fpr%2F1/optimizer.pt
1.05 GB
- Xet hash:
- 931f73d4d199f101c8f10bc5aecd9f36f6638893e1560900ac18801217c2b626
- Size of remote file:
- 1.05 GB
- SHA256:
- 04bd2702ec1eef4f95aec0ea0e23ca88acd8acf1cd8c74c765af6a5dd2790756
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.