Instructions to use hf-internal-testing/tiny-random-MT5ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MT5ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-MT5ForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MT5ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-MT5ForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eeac0b3f556cfd2cac97b64c3173fa4fb1f11123c92c92cd79b3292194dae1d1
- Size of remote file:
- 32.2 MB
- SHA256:
- 222a3b82b41581cc9871420c9864f0082c0a5408e708ed3b3f7012a5c1b68a23
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.