Instructions to use crosso69/bert_lplus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crosso69/bert_lplus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="crosso69/bert_lplus")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("crosso69/bert_lplus") model = AutoModelForSequenceClassification.from_pretrained("crosso69/bert_lplus", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from crosso69/bert_lplus: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/crosso69/bert_lplus/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://crosso69/bert_lplus@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/crosso69/bert_lplus/resolve/refs%2Fpr%2F1/pytorch_model.bin
409 MB
- Xet hash:
- 656376e2a9741f7653bfdc70ee745e16eed5cc7ce75288e303c5c2fddf19a7e8
- Size of remote file:
- 409 MB
- SHA256:
- 84a6d5fdb3d3ab07774ca680c48c062c9291be6798297bdc1b06e9a80dde8a92
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