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")# 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
- Xet hash:
- 048787494239d426ab5e8d19b7f0c0ee89ff27970d8a0c180bc03fd3a11445f6
- Size of remote file:
- 44.8 MB
- SHA256:
- 5b4a581d1054ef31a90cbc6eab468ae298e49219f70b87d3084a7fd621434fa4
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