Instructions to use HasinMDG/Distill-Xsent-Deberta-large-v3-backbone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HasinMDG/Distill-Xsent-Deberta-large-v3-backbone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HasinMDG/Distill-Xsent-Deberta-large-v3-backbone")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HasinMDG/Distill-Xsent-Deberta-large-v3-backbone") model = AutoModelForSequenceClassification.from_pretrained("HasinMDG/Distill-Xsent-Deberta-large-v3-backbone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from HasinMDG/Distill-Xsent-Deberta-large-v3-backbone: direct link, hf CLI and curl.
- Browser
- Download file 1.74 GB
-
https://huggingface.co/HasinMDG/Distill-Xsent-Deberta-large-v3-backbone/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://HasinMDG/Distill-Xsent-Deberta-large-v3-backbone/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/HasinMDG/Distill-Xsent-Deberta-large-v3-backbone/resolve/main/pytorch_model.bin
1.74 GB
- Xet hash:
- 18b682a5816c4041151da510bbd90d8d5e0511985f407844765f68cd810e427e
- Size of remote file:
- 1.74 GB
- SHA256:
- f525b6c71d1561c756af4600804279bd98ef8cd7aba00c7dc2829ba1ef00876d
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