Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use fathyshalaby/emailclassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fathyshalaby/emailclassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fathyshalaby/emailclassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fathyshalaby/emailclassifier") model = AutoModelForSequenceClassification.from_pretrained("fathyshalaby/emailclassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4edbb453d08127ce306f7b61dad874a5171afa021da0fe6db4acb11913e735ca
- Size of remote file:
- 268 MB
- SHA256:
- 5d9a0f384f4e49e4a2495454b2b7f35fefa3935b55e0b0c71449111171d3677d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.