Instructions to use hizella/aBERT_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hizella/aBERT_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hizella/aBERT_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hizella/aBERT_test") model = AutoModelForMaskedLM.from_pretrained("hizella/aBERT_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hizella/aBERT_test: direct link, hf CLI and curl.
- Browser
- Download file 44.9 MB
-
https://huggingface.co/hizella/aBERT_test/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hizella/aBERT_test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hizella/aBERT_test/resolve/main/pytorch_model.bin
44.9 MB
- Xet hash:
- fface7855d381f98dd143a00838a091d1f86fa677c33f24ca53fd3653e1abd2e
- Size of remote file:
- 44.9 MB
- SHA256:
- 56b28f5b09d5d5819711d737fce6e7f9cd86d3688f11dd4a1553ee72816f7614
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