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:
- 8f65d9bb459d1db56eb8ece308aafa2ad8c078d6eef6dd54013a5bd68978a5a2
- Size of remote file:
- 18.2 MB
- SHA256:
- 8fdb11a156c403edace838304ee3d24fe530de7414128d05e284b0b8028c4be8
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