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:
- 2d4297b9ed1a962985d4bafa10535228d52a21bf4c454c6438964fd03b3ca6f9
- Size of remote file:
- 3.38 kB
- SHA256:
- 7f9f17668564f7e448917717cf4fe861072bd56473e554f5c9759463ee700e6a
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