Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use theojolliffe/classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use theojolliffe/classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="theojolliffe/classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("theojolliffe/classifier") model = AutoModelForSequenceClassification.from_pretrained("theojolliffe/classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b006234508352c6ae13dc832ed046ef6bdd062c4a1624f01470e92f7e54244a2
- Size of remote file:
- 3.39 kB
- SHA256:
- bdc2c0ac1729c2aada3f81bc57ac211682accc3b27d49c05924667596966b2eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.