Instructions to use deivism/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deivism/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="deivism/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("deivism/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("deivism/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from deivism/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/deivism/bert-finetuned-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://deivism/bert-finetuned-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/deivism/bert-finetuned-ner/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- b23767e06af6433770f35965fdceec7de8a066b4836b9ba9ce8eb767f3492528
- Size of remote file:
- 5.3 kB
- SHA256:
- 1d584d2a2b30dc5380387b8d2ceede66271c5f13f05efa8f677a0ccb203d18ce
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.