Instructions to use hf-tiny-model-private/tiny-random-RemBertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-RemBertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-RemBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RemBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-RemBertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae0678559b1b05662782e9567f3d4a01c76850700b5af0cedc1f637cdee563b0
- Size of remote file:
- 18.3 MB
- SHA256:
- 463371a3f0c5225259dee625618d5618c1c243017e3c23dc25c4019bdf2d2018
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