Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use philschmid/bert-mini-sst2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/bert-mini-sst2-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="philschmid/bert-mini-sst2-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("philschmid/bert-mini-sst2-distilled") model = AutoModelForSequenceClassification.from_pretrained("philschmid/bert-mini-sst2-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54902c1f49fb4b5331c719ec11ee9ed35abf4a4261dd08dc65de3dafa9190ad7
- Size of remote file:
- 2.93 kB
- SHA256:
- c4c5abbcd630d0ba4c2b44d01f1895ce3e646baa4443bc0f2aa856e1150b8adf
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