Instructions to use nbhimte/tiny-bert-best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nbhimte/tiny-bert-best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nbhimte/tiny-bert-best")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nbhimte/tiny-bert-best") model = AutoModelForSequenceClassification.from_pretrained("nbhimte/tiny-bert-best", device_map="auto") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
TrainOutput(global_step=2456, training_loss=0.29150783277878156, metrics={'train_runtime': 939.2154, 'train_samples_per_second': 167.246, 'train_steps_per_second': 2.615, 'total_flos': 321916620637920.0, 'train_loss': 0.29150783277878156, 'epoch': 4.0})
- Downloads last month
- 4