Instructions to use hf-internal-testing/tiny-random-MraForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MraForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-MraForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MraForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-MraForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 85ba815d1899bd3d6470a632ae51a9a24f06f1500945601051bd5a11904a4200
- Size of remote file:
- 162 kB
- SHA256:
- f3afcab459734ff4c77643801217f58cee4bcf34647a64add47485f95b1161c0
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