Text Classification
Transformers
TensorBoard
Safetensors
bert
classification
mrpc
Generated from Trainer
Instructions to use Mavidart/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mavidart/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mavidart/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mavidart/results") model = AutoModelForSequenceClassification.from_pretrained("Mavidart/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3c8fbfa439a696134eaade0026948ee95173035e32848398edf8d4bbb87f053
- Size of remote file:
- 5.3 kB
- SHA256:
- 8760cca0263284d6231dfc5752e09ea41fbfb2faa7a510c608b6105e4c5bbe5a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.