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:
- c550fb81c42c71415a3738461494f31865dca93d33367d559ea4d9cb5c4d7435
- Size of remote file:
- 181 kB
- SHA256:
- 11bcdc2d04c69937d9ed6249beab5b2617a3eedb187eeb39a6d354aa9c8b6b3b
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