Instructions to use mm/roberta-base-mld with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mm/roberta-base-mld with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mm/roberta-base-mld")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mm/roberta-base-mld") model = AutoModel.from_pretrained("mm/roberta-base-mld", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from mm/roberta-base-mld: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/mm/roberta-base-mld/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://mm/roberta-base-mld/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/mm/roberta-base-mld/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- dd94bddeb5097d3b85dab57d2a15108821ac58eefb17ce5e08dc41e48c946c5e
- Size of remote file:
- 499 MB
- SHA256:
- 1db6438d86396d8ccbba7e483e13d494d5e04b7e16192dd6a0fe49d32db0682d
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