Instructions to use Rohit129/NER_multiconer22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rohit129/NER_multiconer22 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Rohit129/NER_multiconer22")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Rohit129/NER_multiconer22") model = AutoModelForTokenClassification.from_pretrained("Rohit129/NER_multiconer22", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Rohit129/NER_multiconer22: direct link, hf CLI and curl.
- Browser
- Download file 2.02 GB
-
https://huggingface.co/Rohit129/NER_multiconer22/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Rohit129/NER_multiconer22/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Rohit129/NER_multiconer22/resolve/main/pytorch_model.bin
2.02 GB
- Xet hash:
- 940c531c5c26bafba0d611c53c06ada2c7d06a89355fa08b30976f60d9990c48
- Size of remote file:
- 2.02 GB
- SHA256:
- 15ebfb0409408674b5b9ef8614ab0aac0b9430efb654321a490f4131d81226cb
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