Instructions to use MuskanBansal/database-config-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MuskanBansal/database-config-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MuskanBansal/database-config-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("MuskanBansal/database-config-model") model = AutoModelForTokenClassification.from_pretrained("MuskanBansal/database-config-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MuskanBansal/database-config-model: direct link, hf CLI and curl.
- Browser
- Download file 501 MB
-
https://huggingface.co/MuskanBansal/database-config-model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MuskanBansal/database-config-model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MuskanBansal/database-config-model/resolve/main/pytorch_model.bin
501 MB
- Xet hash:
- 934814339622d35cf97572521a57661bfbd94c6ca006b90293458e301cae36c8
- Size of remote file:
- 501 MB
- SHA256:
- 02b86b1313beaed0d899e9e903ed7f3fe304273e8e915649c8a7413e4fd45a4c
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