Instructions to use tiya1012/crossde_distilBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/crossde_distilBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/crossde_distilBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/crossde_distilBERT") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/crossde_distilBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/crossde_distilBERT: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/tiya1012/crossde_distilBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/crossde_distilBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/crossde_distilBERT/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 853fca1457c338d1492b0c79bd737cb9710baf9f8c8ad6bc8bc1cc328ecdefb6
- Size of remote file:
- 268 MB
- SHA256:
- 852ad8de37f1954e963a95776b91a29d5bcc7d26b7fb59194dddd395e2cab04d
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