Instructions to use DavinciTech/BERT_Categorizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DavinciTech/BERT_Categorizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DavinciTech/BERT_Categorizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DavinciTech/BERT_Categorizer") model = AutoModelForSequenceClassification.from_pretrained("DavinciTech/BERT_Categorizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d7116dfe34686b46047b908b2a6bcb1249c1a4f0b5399e8aec9e4d13bd09e90
- Size of remote file:
- 268 MB
- SHA256:
- 4cc8a7de52eede2caebcb8b94fda0afdedc78746d470515bba5b129f086f325e
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