Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use janjibDEV/BERT-rating-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use janjibDEV/BERT-rating-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="janjibDEV/BERT-rating-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("janjibDEV/BERT-rating-classifier") model = AutoModelForSequenceClassification.from_pretrained("janjibDEV/BERT-rating-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 79fc617e688086e6fc9c5e452206668408dd4599c239a8b7524f56a2e34c08c0
- Size of remote file:
- 5.18 kB
- SHA256:
- 756deed1a9bacf171cd872234a211eba7cc5dc44f7f48a2e64940d29c9deb695
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.