Instructions to use ruanchaves/mdeberta-v3-base-faquad-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ruanchaves/mdeberta-v3-base-faquad-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ruanchaves/mdeberta-v3-base-faquad-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ruanchaves/mdeberta-v3-base-faquad-nli") model = AutoModelForSequenceClassification.from_pretrained("ruanchaves/mdeberta-v3-base-faquad-nli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d1b1477923ce1ea73976ac98aa4fb8f85776409720ed955309f37a3957fc312f
- Size of remote file:
- 1.12 GB
- SHA256:
- e17e05b33aa68e4ad94606101793db810f6a4f23ed2f50f4a1f7e9d431fb80be
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.