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-3405/optimizer.pt from jimnoneill/CarD-T: direct link, hf CLI and curl.
- Browser
- Download file 2.66 GB
-
https://huggingface.co/jimnoneill/CarD-T/resolve/main/checkpoint-3405/optimizer.pt
- Command line
-
hf download hf://jimnoneill/CarD-T/checkpoint-3405/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/jimnoneill/CarD-T/resolve/main/checkpoint-3405/optimizer.pt
2.66 GB
- Xet hash:
- e25437831e1be0c6bd45090d8e5dbdc212af3bc0c7d298ec63e83165149e3d73
- Size of remote file:
- 2.66 GB
- SHA256:
- e02e3466dc83c3be17c5a0f2b6c6bf13f136dd1a4acc828e56dc6158fb3039c1
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