Instructions to use greatakela/bert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use greatakela/bert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="greatakela/bert-base-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("greatakela/bert-base-cased") model = AutoModelForTokenClassification.from_pretrained("greatakela/bert-base-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from greatakela/bert-base-cased: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/greatakela/bert-base-cased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://greatakela/bert-base-cased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/greatakela/bert-base-cased/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 4b0fe88ed88c153d4fb78b8ad3a9f3dd7be9eca8376cd2c60a7d18abd39522b9
- Size of remote file:
- 431 MB
- SHA256:
- 38ed4252a7285820a30423d42d49a54c541d2b4843994a77704a4952c48604e5
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