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")# 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
- Xet hash:
- a769850f83824d138225d53e94d0b00b20477e105566045d890d866df3b5fa8d
- Size of remote file:
- 4.03 kB
- SHA256:
- c4388dea3d72417a1862a86190a4bd78d4b980194993f5e27db47d59074c0399
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