Instructions to use Harini2506/second_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harini2506/second_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Harini2506/second_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Harini2506/second_model") model = AutoModelForTokenClassification.from_pretrained("Harini2506/second_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Harini2506/second_model: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/Harini2506/second_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Harini2506/second_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Harini2506/second_model/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- 077c3a5094f316b84afcbe27286dc588133799070f380bf483a16cc4514d8fa4
- Size of remote file:
- 437 MB
- SHA256:
- 0e706dd000bfccc7d826ef80c8a09be898ee12390cc388b8c8d4844e872a62fb
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