Instructions to use LDD/bert_from_scratch_wwm_new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LDD/bert_from_scratch_wwm_new with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LDD/bert_from_scratch_wwm_new")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LDD/bert_from_scratch_wwm_new") model = AutoModel.from_pretrained("LDD/bert_from_scratch_wwm_new", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from LDD/bert_from_scratch_wwm_new: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/LDD/bert_from_scratch_wwm_new/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://LDD/bert_from_scratch_wwm_new/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LDD/bert_from_scratch_wwm_new/resolve/main/pytorch_model.bin
409 MB
- Xet hash:
- 165b2c2281ff87200e7862ecb7299aebe11555250f73b54d6fba171b605bac49
- Size of remote file:
- 409 MB
- SHA256:
- 7b30ef1346461b6617c97099b0e5b6c1a68786de8ece2c0b8fc348d4cdc5b3b1
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