Instructions to use microsoft/deberta-base-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/deberta-base-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="microsoft/deberta-base-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-base-mnli") model = AutoModelForSequenceClassification.from_pretrained("microsoft/deberta-base-mnli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download bpe_encoder.bin from microsoft/deberta-base-mnli: direct link, hf CLI and curl.
- Browser
- Download file 3.92 MB
-
https://huggingface.co/microsoft/deberta-base-mnli/resolve/69891cc5e0bcde6ce0fa9e6889df3321443095a0/bpe_encoder.bin
- Command line
-
hf download hf://microsoft/deberta-base-mnli@69891cc5e0bcde6ce0fa9e6889df3321443095a0/bpe_encoder.bin
-
curl -L -o bpe_encoder.bin https://huggingface.co/microsoft/deberta-base-mnli/resolve/69891cc5e0bcde6ce0fa9e6889df3321443095a0/bpe_encoder.bin
3.92 MB
- Xet hash:
- 6393ca7c4f74fc91cefb3897de66371c05d277d9f046d5e0468d8fb63a58a691
- Size of remote file:
- 3.92 MB
- SHA256:
- e7c6f9eecb461c01e09c00656ccf3e27944b9e74bfe29e51632b13d3cd9d6c8e
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