Instructions to use Anery/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Anery/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Anery/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Anery/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("Anery/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Anery/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 3.18 kB
-
https://huggingface.co/Anery/bert-finetuned-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://Anery/bert-finetuned-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Anery/bert-finetuned-ner/resolve/main/training_args.bin
3.18 kB
- Xet hash:
- 803870d76d17bb0071bdbe8c8008893f7c26b65375a1f6df4823d79f9b9eb9c7
- Size of remote file:
- 3.18 kB
- SHA256:
- 1e2880a989fde52b73f294ac2e483cad40abc83f0dacf0b8452e0032a5d7e572
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.