Instructions to use hfl/english-pert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/english-pert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hfl/english-pert-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hfl/english-pert-base") model = AutoModel.from_pretrained("hfl/english-pert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hfl/english-pert-base: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/hfl/english-pert-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hfl/english-pert-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hfl/english-pert-base/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- d96d7af923ee74a780d20a68d57605c17c4fa0eaada087f8ad6fdcbe8643741f
- Size of remote file:
- 440 MB
- SHA256:
- 500284f5bd126e05e4bd8dc6729705f9f79634af17140a3aa2f49a639de6c4d4
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