Instructions to use MLMvsCLM/210m-mlm20-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/210m-mlm20-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/210m-mlm20-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/210m-mlm20-42k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f2f2d38b345e9b4cbe688c2cd3642697a0ede23d68f710bf3d7a9d794a1d66f6
- Size of remote file:
- 1.24 GB
- SHA256:
- ef818c6345767199ee8733d684177dbe64dd0bcd12210d07136d7ff929f20fcb
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