Instructions to use MLMvsCLM/210m-mlm50-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/210m-mlm50-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/210m-mlm50-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/210m-mlm50-42k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 345d661e935f3d5dace8551ce717453cb0897ea1d9c382a5dcfa86970cda7908
- Size of remote file:
- 1.24 GB
- SHA256:
- 2b7f9bd8333b6b2fd7a342f9cd8230730d7d09084dd88b3d5f27f96470ea3f07
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