Instructions to use StevenLimcorn/bert-large-uncased-semeval2016-laptops with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StevenLimcorn/bert-large-uncased-semeval2016-laptops with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="StevenLimcorn/bert-large-uncased-semeval2016-laptops")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("StevenLimcorn/bert-large-uncased-semeval2016-laptops") model = AutoModelForMaskedLM.from_pretrained("StevenLimcorn/bert-large-uncased-semeval2016-laptops", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 129 Bytes
4232aed | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:c4388dea3d72417a1862a86190a4bd78d4b980194993f5e27db47d59074c0399
size 4027
|