Instructions to use dslim/distilbert-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dslim/distilbert-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dslim/distilbert-NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dslim/distilbert-NER") model = AutoModelForTokenClassification.from_pretrained("dslim/distilbert-NER", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dslim/distilbert-NER: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/dslim/distilbert-NER/resolve/main/training_args.bin
- Command line
-
hf download hf://dslim/distilbert-NER/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dslim/distilbert-NER/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 6292d42600a5ab5494c589cfe1f1312154ebf1b37570354cf5d17fc301285a2f
- Size of remote file:
- 4.54 kB
- SHA256:
- 2fea728bc1262878cb9e5fec0154073ab508564d4c470a6ad72082a3f706fe4b
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