Instructions to use chiendvhust/cross-encoder-distilroberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chiendvhust/cross-encoder-distilroberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chiendvhust/cross-encoder-distilroberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chiendvhust/cross-encoder-distilroberta-base") model = AutoModelForSequenceClassification.from_pretrained("chiendvhust/cross-encoder-distilroberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from chiendvhust/cross-encoder-distilroberta-base: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/chiendvhust/cross-encoder-distilroberta-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://chiendvhust/cross-encoder-distilroberta-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/chiendvhust/cross-encoder-distilroberta-base/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- c0254e56725c493e0ae2eeacb75a3afb4e642d67f1f2b28e5e74a24e8f88a0f2
- Size of remote file:
- 329 MB
- SHA256:
- eef43458fd651b8298e6a8484ffcdc3e21ea68c7e5f4e8774b4b0b95cabed636
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