Instructions to use hf-tiny-model-private/tiny-random-SqueezeBertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-SqueezeBertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-SqueezeBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-SqueezeBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-SqueezeBertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3da530639e9a1b1710c8b34f2606b422e56406aa32443052121238644501ce6
- Size of remote file:
- 328 kB
- SHA256:
- 2d0884e62db80c2b28141734f02109667b860b5337247a216a839fe862d467a6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.