Instructions to use MLMvsCLM/610m-mlm50-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-mlm50-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-mlm50-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-mlm50-42k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6888b53246ca4e07c869a72f622e6b8ff1ebdaf3325a8e2efc177afe848c434
- Size of remote file:
- 3.02 GB
- SHA256:
- 3d7ef1ccd03a15b4218f56b89fba65822a3fa6f98f2e7f772f5dc42f1c5b29a0
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