Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use JTH/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JTH/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JTH/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JTH/results") model = AutoModelForSequenceClassification.from_pretrained("JTH/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a8abda97b14ce7733a3764fb46f4ec0dfb8c11f2bb5fcd6daa684efd0be394f
- Size of remote file:
- 3.31 kB
- SHA256:
- 7d29d35d30cee7ffd04947f4881cc12f3f0118664452d0fdee9b70be5c415bea
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.