Instructions to use sangmichaelxie/randomselect-bert-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sangmichaelxie/randomselect-bert-scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sangmichaelxie/randomselect-bert-scratch")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sangmichaelxie/randomselect-bert-scratch") model = AutoModel.from_pretrained("sangmichaelxie/randomselect-bert-scratch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sangmichaelxie/randomselect-bert-scratch: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/sangmichaelxie/randomselect-bert-scratch/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://sangmichaelxie/randomselect-bert-scratch@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sangmichaelxie/randomselect-bert-scratch/resolve/refs%2Fpr%2F1/pytorch_model.bin
438 MB
- Xet hash:
- 0c0071412726ebc0ffc9633e0bcea1ffd80f03f33d7c3559b6d4306dd8299459
- Size of remote file:
- 438 MB
- SHA256:
- 9ad17989c49a8e68e81d849fc9f7d9853e53d884f936becd35b7dfd1da02bb37
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