Text Classification
Transformers
PyTorch
English
funding-extraction
arxiv
scholarly-communication
chunk-classification
modernbert
Instructions to use cometadata/funding-chunk-classifier-modernbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cometadata/funding-chunk-classifier-modernbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cometadata/funding-chunk-classifier-modernbert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cometadata/funding-chunk-classifier-modernbert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Initial upload: ModernBERT-base chunk classifier (stage 1 of funding-extraction cascade)
f69ad93 verified Download pytorch_model.bin from cometadata/funding-chunk-classifier-modernbert-base: direct link, hf CLI and curl.
- Browser
- Download file 596 MB
-
https://huggingface.co/cometadata/funding-chunk-classifier-modernbert-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cometadata/funding-chunk-classifier-modernbert-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cometadata/funding-chunk-classifier-modernbert-base/resolve/main/pytorch_model.bin
596 MB
- Xet hash:
- 5589bf861342992cabe375741d546f8965a245f6876900514683c5082257cdec
- Size of remote file:
- 596 MB
- SHA256:
- 566e172d7db3c9201011533a74503592384882622568f171b27bd47f1708e5ba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.