Instructions to use hf-tiny-model-private/tiny-random-RemBertModel 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-RemBertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-RemBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RemBertModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-RemBertModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a5e1f57912e5e0a929906a5c064f67c6c50e29ba97333e9791b809cfda8e1548
- Size of remote file:
- 18.2 MB
- SHA256:
- 1dfb63a61b4bd7f9faa8e84905b33fa64d274470ee4a0a3b52652e5f9e1134a5
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