Instructions to use tmatsuda/bert-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmatsuda/bert-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmatsuda/bert-test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmatsuda/bert-test") model = AutoModelForSequenceClassification.from_pretrained("tmatsuda/bert-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edd9efca5c979c80608d6c1775a7fa52efe0f715fd71e570f6804492f422a3a5
- Size of remote file:
- 433 MB
- SHA256:
- 8913fc35f5b3355700263ad8465bf873f811bebd916aeeebb09287069d08a099
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.