Instructions to use blekkk/ner_411_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blekkk/ner_411_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="blekkk/ner_411_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("blekkk/ner_411_2") model = AutoModelForTokenClassification.from_pretrained("blekkk/ner_411_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from blekkk/ner_411_2: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/blekkk/ner_411_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://blekkk/ner_411_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/blekkk/ner_411_2/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- b96e10f7d82b4ec37429276dbd90e66e6c272376eb13f2d3229f0fb105676fc5
- Size of remote file:
- 1.11 GB
- SHA256:
- dd8df28d9138f470aec44aba514fb7d51e92f1f2738d229e18946ebcc743e5bd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.