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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0598a88349d2d1713635a8d668acbeeb5c5f578f2c03d1f1ef4f1a4629365b6b
- Size of remote file:
- 181 kB
- SHA256:
- 7384f49614b43cabf23ae8da0353df21b31fa213b48c74a2481bde952c0b807b
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