Token Classification
Transformers
TensorBoard
Safetensors
English
electra
biology
chemistry
medical
cancer
carcinogenesis
biomedical
ner
oncology
Eval Results (legacy)
Instructions to use jimnoneill/CarD-T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jimnoneill/CarD-T with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jimnoneill/CarD-T")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jimnoneill/CarD-T") model = AutoModelForTokenClassification.from_pretrained("jimnoneill/CarD-T", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-3204/training_args.bin from jimnoneill/CarD-T: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/jimnoneill/CarD-T/resolve/main/checkpoint-3204/training_args.bin
- Command line
-
hf download hf://jimnoneill/CarD-T/checkpoint-3204/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jimnoneill/CarD-T/resolve/main/checkpoint-3204/training_args.bin
5.05 kB
- Xet hash:
- 378f279b05dae14e29de99726c61672e6d8877dc90dee7d3026cf98a6cb65b47
- Size of remote file:
- 5.05 kB
- SHA256:
- 36534a66cd54fa62d55a78ae7da66791486f97dbf75f55360327b517cd4e0be3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.