Instructions to use luntomas/mdeberta-v3-base-pre-filter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luntomas/mdeberta-v3-base-pre-filter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="luntomas/mdeberta-v3-base-pre-filter")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("luntomas/mdeberta-v3-base-pre-filter") model = AutoModelForSequenceClassification.from_pretrained("luntomas/mdeberta-v3-base-pre-filter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from luntomas/mdeberta-v3-base-pre-filter: direct link, hf CLI and curl.
- Browser
- Download file 16.4 MB
-
https://huggingface.co/luntomas/mdeberta-v3-base-pre-filter/resolve/main/tokenizer.json
- Command line
-
hf download hf://luntomas/mdeberta-v3-base-pre-filter/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/luntomas/mdeberta-v3-base-pre-filter/resolve/main/tokenizer.json
16.4 MB
- Xet hash:
- ec173449f6efc0a110d29ddab7f80373e8a08412da5c7a209f4167f59ae9b50a
- Size of remote file:
- 16.4 MB
- SHA256:
- 2e12ea7ef3c1bfd4b1fb977636226680b9fcf10ede02f869dec0a324a5b86dfb
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