Instructions to use liamvbetts/SNH_BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liamvbetts/SNH_BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="liamvbetts/SNH_BERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("liamvbetts/SNH_BERT") model = AutoModelForSequenceClassification.from_pretrained("liamvbetts/SNH_BERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a7da37100bead71c3294c5607cdec1e90e059ce0852f1e58dc273cebb5ad12a
- Size of remote file:
- 433 MB
- SHA256:
- 6a19f594c5049da407b8b48bed4da7597696f38d0dbcaee16b179d13d842ab77
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