Instructions to use hf-internal-testing/tiny-random-SqueezeBertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SqueezeBertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-SqueezeBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SqueezeBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-SqueezeBertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dcba6a093ec95296c2937c0b650e27c9c81374fe5391acc1c27cd5e9fb51da0b
- Size of remote file:
- 328 kB
- SHA256:
- 93bc103955c49d1206e90b5a72c8634dc332ed6c5a8fe7044f79866da3e0f9c7
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