Token Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
Instructions to use Perriewang/distilbert-base-uncased-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Perriewang/distilbert-base-uncased-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Perriewang/distilbert-base-uncased-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Perriewang/distilbert-base-uncased-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("Perriewang/distilbert-base-uncased-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Perriewang/distilbert-base-uncased-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/Perriewang/distilbert-base-uncased-finetuned-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://Perriewang/distilbert-base-uncased-finetuned-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Perriewang/distilbert-base-uncased-finetuned-ner/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- efd3f5dbae95de226f42c9ae49f642f91dec0b6aec91311c2286ea89a5002134
- Size of remote file:
- 5.18 kB
- SHA256:
- f8747c51e2fc691916bee761e8e30abb8ff66de27fe5db94f15593fce2a354c2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.