Instructions to use kSaluja/test-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kSaluja/test-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kSaluja/test-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kSaluja/test-ner") model = AutoModelForTokenClassification.from_pretrained("kSaluja/test-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kSaluja/test-ner: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/kSaluja/test-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kSaluja/test-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kSaluja/test-ner/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- b62282b52792aea8633b145bd3961f4c57389463c2a21229ae204abd69c7b4fa
- Size of remote file:
- 436 MB
- SHA256:
- c205744317865c1e2e7a254862fd87aaf7dfff363d37695e889db1179b9bf28c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.