Instructions to use MLMvsCLM/1b-mlm50-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/1b-mlm50-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/1b-mlm50-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/1b-mlm50-42k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38121690006a8ce39f8dd8cf4ffad4ea92600fa73e90a67a3a45543a23f736d3
- Size of remote file:
- 5.64 GB
- SHA256:
- 53e147c8c2be0f79fc4c4bd5055a51bd8fb6f83d41a80fcc1881ece2481eaad7
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