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 tokenizer.json from rwillh11/base-mdbertav3-token-classification-groups-2.0: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/rwillh11/base-mdbertav3-token-classification-groups-2.0/resolve/main/tokenizer.json
- Command line
-
hf download hf://rwillh11/base-mdbertav3-token-classification-groups-2.0/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/rwillh11/base-mdbertav3-token-classification-groups-2.0/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 533b16e22de4462f140f0f7832cedfcff28825d75445a5b2dead760ae39a351a
- Size of remote file:
- 16.3 MB
- SHA256:
- bac156480420f378c878f1d12610f5f70c0d4a257e2f9ecd0a3a8ff9fb34f3ae
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