Instructions to use rwillh11/base-mdbertav3-token-classification-groups-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rwillh11/base-mdbertav3-token-classification-groups-2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rwillh11/base-mdbertav3-token-classification-groups-2.0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("rwillh11/base-mdbertav3-token-classification-groups-2.0") model = AutoModelForTokenClassification.from_pretrained("rwillh11/base-mdbertav3-token-classification-groups-2.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rwillh11/base-mdbertav3-token-classification-groups-2.0: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/rwillh11/base-mdbertav3-token-classification-groups-2.0/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rwillh11/base-mdbertav3-token-classification-groups-2.0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rwillh11/base-mdbertav3-token-classification-groups-2.0/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 334d6bf3d77bd36f7cb2b2509461da15c565467e848b2bcb0998454439fb8693
- Size of remote file:
- 1.11 GB
- SHA256:
- 8b340785e524d6bcbc5923c188f46b08cc74e688dd4566e5ef9ead86c39a4619
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