Instructions to use MrKustic/dl2-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MrKustic/dl2-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MrKustic/dl2-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("MrKustic/dl2-ner") model = AutoModelForTokenClassification.from_pretrained("MrKustic/dl2-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from MrKustic/dl2-ner: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/MrKustic/dl2-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://MrKustic/dl2-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/MrKustic/dl2-ner/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- 7ee69f92676720d0da20b061ff955cbeb4477c45fbc7b92aab055ffb36f89338
- Size of remote file:
- 5.78 kB
- SHA256:
- fb3a6738eae37e6423daa54ac3091d51a7b8620bed56d131112380722866df9d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.