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:
- 7ccf426f8e048379680addfc20cc85f1eb0025e538a05320f2c2df00a5b32af9
- Size of remote file:
- 181 kB
- SHA256:
- 71cb91b4d4a3739ca9d23f25684496aec5cea0d6f369baccd55b7f2dbe3a9def
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