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
Download pytorch_model.bin from StevenLimcorn/bert-large-uncased-semeval2016-laptops: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/StevenLimcorn/bert-large-uncased-semeval2016-laptops/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://StevenLimcorn/bert-large-uncased-semeval2016-laptops/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/StevenLimcorn/bert-large-uncased-semeval2016-laptops/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- c03298f88bffd1da0f5a79bdbe31c4db60d37b0077d15958d6d4cbd8845c09da
- Size of remote file:
- 1.34 GB
- SHA256:
- 81a6d3a7543ee99076aa900cfb29aee8584a1aca4e29722597d59a294fe351e3
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