Instructions to use apasi/BertClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apasi/BertClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="apasi/BertClassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("apasi/BertClassifier") model = AutoModelForSequenceClassification.from_pretrained("apasi/BertClassifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a57730865bca14c76d505e57e69a85e92a3ac7b959caa47e992c9b60f7cba91c
- Size of remote file:
- 438 MB
- SHA256:
- 9d85eba5cfeaa3a6e75278e126a9f50d440f3d79f051794655b6670b94808f2f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.