Instructions to use MLMvsCLM/1b-mlm20-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/1b-mlm20-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/1b-mlm20-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/1b-mlm20-42k", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 27daae80d07cf122d93d5b787002c188d695ab80d5bd261b1cce758d76f08787
- Size of remote file:
- 5.64 GB
- SHA256:
- 8d11236fa6912855162c0ee557cdb3baa3baf92abf6e12ff3f35d7e65c27e953
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