Instructions to use rwillh11/mdeberta_NLI_policy_AlignedQuote with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rwillh11/mdeberta_NLI_policy_AlignedQuote with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rwillh11/mdeberta_NLI_policy_AlignedQuote")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rwillh11/mdeberta_NLI_policy_AlignedQuote") model = AutoModelForSequenceClassification.from_pretrained("rwillh11/mdeberta_NLI_policy_AlignedQuote", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from rwillh11/mdeberta_NLI_policy_AlignedQuote: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/rwillh11/mdeberta_NLI_policy_AlignedQuote/resolve/main/tokenizer.json
- Command line
-
hf download hf://rwillh11/mdeberta_NLI_policy_AlignedQuote/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/rwillh11/mdeberta_NLI_policy_AlignedQuote/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 7e9d0ed494d25ab5a1bb3ad2e0338974a72e5438cb4cee28928034dde70210f1
- Size of remote file:
- 16.3 MB
- SHA256:
- ac9bd80d04d21b4df95917bdf7c750cbea28acc30a4462c76ca2b9c86d371863
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