Instructions to use hf-internal-testing/tiny-random-NystromformerForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-NystromformerForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-NystromformerForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-NystromformerForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-NystromformerForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b43185a6b9d7ca600edd139126da87febbb58c98f13b207978f9a0ad9bbab4e3
- Size of remote file:
- 4.07 MB
- SHA256:
- 081b812357235d8f2cdec7ffcafc7e5d24ae2e9940acb1c20e36d411f631acb2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.