Text Classification
Transformers
TensorBoard
Safetensors
albert
Generated from Trainer
sentiment-analysis
Instructions to use DerivedFunction01/albert-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DerivedFunction01/albert-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DerivedFunction01/albert-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DerivedFunction01/albert-imdb") model = AutoModelForSequenceClassification.from_pretrained("DerivedFunction01/albert-imdb") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dadbcace49de75c7fc7263b999903bd48de90e582ad11bbf27f73f750598683a
- Size of remote file:
- 46.7 MB
- SHA256:
- 7cfce5a9e13027c90024096abbf60cf161ec15aa362dbad4833663d8b657be50
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.