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") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 051140d23dff6d6fb4433af56aed9f3bfb320eb8b23a978fe5542cf184c3db91
- Size of remote file:
- 17.6 MB
- SHA256:
- e76203837509647859ac1f01a7cce7b48d09999ccae518040ec47f17cc31d58b
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