Instructions to use brao/innox-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brao/innox-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="brao/innox-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("brao/innox-bert") model = AutoModelForTokenClassification.from_pretrained("brao/innox-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from brao/innox-bert: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/brao/innox-bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://brao/innox-bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/brao/innox-bert/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- 47f0b796fb58a825da24ccac50bc03128b704a0164c76dfef7280c7d444d0284
- Size of remote file:
- 1.34 GB
- SHA256:
- 89867acad0de4ecc45896e74fb6aab16c2ca6b6f758df173f7e41d48b7c2b1b9
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