Instructions to use mm/roberta-large-mld with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mm/roberta-large-mld with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mm/roberta-large-mld")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mm/roberta-large-mld") model = AutoModel.from_pretrained("mm/roberta-large-mld", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from mm/roberta-large-mld: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/mm/roberta-large-mld/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://mm/roberta-large-mld/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/mm/roberta-large-mld/resolve/main/flax_model.msgpack
1.42 GB
- Xet hash:
- e2644f0714be7db33cc1b605ad0da584c808de58cdcae6664b8f88d2e1bc094e
- Size of remote file:
- 1.42 GB
- SHA256:
- 12de09f99b9c32c4a8d6c7204085f7a8fdc5487ab59e5164f94e67bef15cd03b
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