Instructions to use visoc/BERTMiniESPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use visoc/BERTMiniESPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="visoc/BERTMiniESPT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("visoc/BERTMiniESPT") model = AutoModelForMaskedLM.from_pretrained("visoc/BERTMiniESPT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from visoc/BERTMiniESPT: direct link, hf CLI and curl.
- Browser
- Download file 44.8 MB
-
https://huggingface.co/visoc/BERTMiniESPT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://visoc/BERTMiniESPT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/visoc/BERTMiniESPT/resolve/main/pytorch_model.bin
44.8 MB
- Xet hash:
- 048787494239d426ab5e8d19b7f0c0ee89ff27970d8a0c180bc03fd3a11445f6
- Size of remote file:
- 44.8 MB
- SHA256:
- 5b4a581d1054ef31a90cbc6eab468ae298e49219f70b87d3084a7fd621434fa4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.