Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use philschmid/tiny-bert-sst2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/tiny-bert-sst2-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="philschmid/tiny-bert-sst2-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("philschmid/tiny-bert-sst2-distilled") model = AutoModelForSequenceClassification.from_pretrained("philschmid/tiny-bert-sst2-distilled", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f2e0dc69d7859f7cf432d0981e72e75feba9e26cb061fb19dcbafd5442e331bc
- Size of remote file:
- 2.93 kB
- SHA256:
- 2ac2c54b3101d398e615da3063675f42d87d6058d72aaf641be336a7503c3804
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