Instructions to use VityaVitalich/bert-tiny-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VityaVitalich/bert-tiny-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VityaVitalich/bert-tiny-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VityaVitalich/bert-tiny-sst2") model = AutoModelForSequenceClassification.from_pretrained("VityaVitalich/bert-tiny-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3257f744f0aced25477fd1a83e8bcaa96da3de516c3de06807239086ff3aa62f
- Size of remote file:
- 4.03 kB
- SHA256:
- 650627c715c48a10e613895418dbc5644f7224d3de7b226af5fdeb3b0965a650
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.