Instructions to use kushaljoseph/tiny-bert-sst2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kushaljoseph/tiny-bert-sst2-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kushaljoseph/tiny-bert-sst2-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kushaljoseph/tiny-bert-sst2-distilled") model = AutoModelForSequenceClassification.from_pretrained("kushaljoseph/tiny-bert-sst2-distilled") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5223ed8aeb80971e394960d1eda4ee49b643e50a47c95914b5021e70fbc1cffa
- Size of remote file:
- 3.12 kB
- SHA256:
- 5574e4dca604159469c1dd2d57a5d3486d17178db699247e1abe57207b8aa676
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.