Instructions to use fktime/dbert_ai4p with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fktime/dbert_ai4p with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fktime/dbert_ai4p")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("fktime/dbert_ai4p") model = AutoModelForTokenClassification.from_pretrained("fktime/dbert_ai4p", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from fktime/dbert_ai4p: direct link, hf CLI and curl.
- Browser
- Download file 1.08 GB
-
https://huggingface.co/fktime/dbert_ai4p/resolve/main/optimizer.pt
- Command line
-
hf download hf://fktime/dbert_ai4p/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/fktime/dbert_ai4p/resolve/main/optimizer.pt
1.08 GB
- Xet hash:
- 214071d1ce7c7e8021b3e03ed3f056d6ec9493da4ec561801779c98cc2811dd1
- Size of remote file:
- 1.08 GB
- SHA256:
- df6d8224814605d5069fdc4d9f2b450dec1932b429ee3caa739b1ae975cd7303
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