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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30155c5fd92f6c43ce9222bc89b48af010ed42d4e35e2bbce34c7cb550685a15
- Size of remote file:
- 33.2 MB
- SHA256:
- 297a9e50d42bf091d38d7313903d8417ae2ecbd8dda869cd386bac9d6303e819
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