Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use jysh1023/tiny-bert-sst2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jysh1023/tiny-bert-sst2-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jysh1023/tiny-bert-sst2-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jysh1023/tiny-bert-sst2-distilled") model = AutoModelForSequenceClassification.from_pretrained("jysh1023/tiny-bert-sst2-distilled") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
- 7.99 kB xet
- 8.01 kB xet
- 6.57 kB xet
- 8.01 kB xet
- 8.49 kB xet
- 6.09 kB xet
- 6.69 kB xet
- 4.77 kB xet
- 4.77 kB xet
- 6.69 kB xet
- 4.77 kB xet
- 6.21 kB xet
- 6.69 kB xet
- 4.77 kB xet
- 6.69 kB xet
- 4.77 kB xet
- 5.61 kB xet
- 5.61 kB xet
- 5.61 kB xet
- 5.73 kB xet
- 6.69 kB xet
- 6.09 kB xet
- 5.61 kB xet
- 6.09 kB xet
- 5.25 kB xet
- 6.21 kB xet
- 6.69 kB xet
- 4.77 kB xet
- 5.61 kB xet
- 5.25 kB xet
- 5.25 kB xet
- 4.77 kB xet
- 4.77 kB xet
- 5.61 kB xet
- 4.77 kB xet
- 4.77 kB xet
- 4.77 kB xet
- 4.77 kB xet
- 6.69 kB xet
- 6.57 kB xet
- 4.77 kB xet
- 4.77 kB xet
- 5.61 kB xet
- 5.61 kB xet
- 5.61 kB xet
- 6.09 kB xet
- 4.77 kB xet
- 4.77 kB xet
- 4.77 kB xet
- 4.77 kB xet