Instructions to use huwendeng/distilroberta_b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huwendeng/distilroberta_b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="huwendeng/distilroberta_b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("huwendeng/distilroberta_b") model = AutoModelForSequenceClassification.from_pretrained("huwendeng/distilroberta_b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from huwendeng/distilroberta_b: direct link, hf CLI and curl.
- Browser
- Download file 328 MB
-
https://huggingface.co/huwendeng/distilroberta_b/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://huwendeng/distilroberta_b/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/huwendeng/distilroberta_b/resolve/main/flax_model.msgpack
328 MB
- Xet hash:
- 080bfc7bf43208a19038e80a4ccd760f815e42c47fa0b789b952505c3e2ecbce
- Size of remote file:
- 328 MB
- SHA256:
- cfc1ac44d53c17443d86b2dba7ecfdfab64384a8a8aeba37bf6253f3fd092e93
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