Instructions to use hf-internal-testing/tiny-random-FNetForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FNetForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-FNetForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-FNetForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-FNetForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 85d60681ee77daf5260bdc1eca05df696190fc5192c987a80fff3c0c72734a07
- Size of remote file:
- 4.23 MB
- SHA256:
- c5c518ba4b46eadf236f142fbe1234c98f86fa19988827ef5387db72e675b327
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