Instructions to use YaHi/teacher_electra_small_building_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YaHi/teacher_electra_small_building_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YaHi/teacher_electra_small_building_binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YaHi/teacher_electra_small_building_binary") model = AutoModelForSequenceClassification.from_pretrained("YaHi/teacher_electra_small_building_binary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 555d2b2f5b131a06038d95453ed1dcae13ca438f2bc0d69efea0b4f9838b561d
- Size of remote file:
- 4.47 kB
- SHA256:
- f5e5ac016d0b7e61ba1432a2f69ad81c2838d7a4c063890d2e99a8449c395f92
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.