Instructions to use hf-internal-testing/tiny-random-UMT5ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UMT5ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-UMT5ForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-UMT5ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-UMT5ForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3aac2e1c1a62c803761c4e16a7d3f465cc880408f77dc0d7556b7ff104eaec3b
- Size of remote file:
- 33.2 MB
- SHA256:
- 8a3766fce4e82b6cc41ecfff5c8af1920d4bc373c065dcd07010f3fe19647feb
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