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:
- bfa17fa7ef36fd003a4991e8866f019080a6e42e195a94cca44ebba197cbbaf0
- Size of remote file:
- 44.7 MB
- SHA256:
- c71f69889a0d0bde47d67b6acb1eb683f346d59d1ff956b252ee55dac81f509c
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