Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use heriosousa/imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use heriosousa/imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="heriosousa/imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("heriosousa/imdb") model = AutoModelForSequenceClassification.from_pretrained("heriosousa/imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c27c039b77e99d12ff89bd0e3a836ee6eb1c6c5febe2fc8a97677b5ba474fcfc
- Size of remote file:
- 433 MB
- SHA256:
- 0c4b88bdfc968419b9987b376b8e1410a26133b4a4e272d5ca4a77f29d589907
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.