Instructions to use inesani/ner-test3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inesani/ner-test3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="inesani/ner-test3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("inesani/ner-test3") model = AutoModelForTokenClassification.from_pretrained("inesani/ner-test3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from inesani/ner-test3: direct link, hf CLI and curl.
- Browser
- Download file 266 MB
-
https://huggingface.co/inesani/ner-test3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://inesani/ner-test3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/inesani/ner-test3/resolve/main/pytorch_model.bin
266 MB
- Xet hash:
- dc8f214744519aedb38d624e8fad99e85e629b3eb9742ddb0c27d3b1e016dc55
- Size of remote file:
- 266 MB
- SHA256:
- 5b703d6e01a15d0f92fd8cb63d4b3267abb3a60d12c5f863c6f43727295a2af4
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