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
- Xet hash:
- 2af15b4aab261a6990c89116e4b2253c5ecabbee2a97da1adeeea67f3838c535
- Size of remote file:
- 3.12 kB
- SHA256:
- 086d3e3aef97b56279a3dda08fa4431613aa37ba919c8a4738b9ae45ba42d174
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