Instructions to use MLMvsCLM/210m-mlm30-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/210m-mlm30-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/210m-mlm30-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/210m-mlm30-42k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 634e35c68c18ca80ba820afc9934d431e920e4cb68e776a556dba29af80f69d2
- Size of remote file:
- 1.24 GB
- SHA256:
- 15beab00fae3c537d1f55271d690785c48a4c379591bb99f387b4223383c9874
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