Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Rami/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rami/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rami/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rami/results") model = AutoModelForSequenceClassification.from_pretrained("Rami/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 00d4178c8fb04a30e5429f358be245678df5b832ae5d254e47af5c7ef3be432d
- Size of remote file:
- 268 MB
- SHA256:
- 2168d77bdca7457d4269d67d377f5e5db9744e994a50de9a3541e333a5c5df7b
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