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:
- fe2b0b6317eccd1231bd1c1e6bc4755c4e4ef5cd73a9f5edde28248a38f906c1
- Size of remote file:
- 181 kB
- SHA256:
- 2238718fdc7957f2e9aa26e7333e7a800b279c30e003956a9711fd65c75b4c71
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