Instructions to use rwillh11/mdeberta_groups_2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rwillh11/mdeberta_groups_2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rwillh11/mdeberta_groups_2.0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rwillh11/mdeberta_groups_2.0") model = AutoModelForSequenceClassification.from_pretrained("rwillh11/mdeberta_groups_2.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from rwillh11/mdeberta_groups_2.0: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/rwillh11/mdeberta_groups_2.0/resolve/main/tokenizer.json
- Command line
-
hf download hf://rwillh11/mdeberta_groups_2.0/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/rwillh11/mdeberta_groups_2.0/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- cfb337e346fac3981f84bb47953dad05d36450cbc3ab32ece9b18d2f4a4aa8fe
- Size of remote file:
- 16.3 MB
- SHA256:
- 66dd68f27278061fc1702ba512fb2ec7c41c24eebf27dc5a9cc95d998166f0c7
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