Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use desonglll/test-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use desonglll/test-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="desonglll/test-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("desonglll/test-ner") model = AutoModelForTokenClassification.from_pretrained("desonglll/test-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-3000/optimizer.pt from desonglll/test-ner: direct link, hf CLI and curl.
- Browser
- Download file 871 MB
-
https://huggingface.co/desonglll/test-ner/resolve/main/checkpoint-3000/optimizer.pt
- Command line
-
hf download hf://desonglll/test-ner/checkpoint-3000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/desonglll/test-ner/resolve/main/checkpoint-3000/optimizer.pt
871 MB
- Xet hash:
- b80c96109b9e9f7c1371a8e730051ff5bbcdb371c6206955ff3ec94dfc68beaa
- Size of remote file:
- 871 MB
- SHA256:
- 2d4cc3ba9a368c14b65feffd150ee7aa16aac5c35e61bd0d704f7353de3473ce
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.