Instructions to use docketanalyzer/distilroberta-base-ddlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use docketanalyzer/distilroberta-base-ddlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="docketanalyzer/distilroberta-base-ddlm")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("docketanalyzer/distilroberta-base-ddlm") model = AutoModel.from_pretrained("docketanalyzer/distilroberta-base-ddlm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from docketanalyzer/distilroberta-base-ddlm: direct link, hf CLI and curl.
- Browser
- Download file 328 MB
-
https://huggingface.co/docketanalyzer/distilroberta-base-ddlm/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://docketanalyzer/distilroberta-base-ddlm/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/docketanalyzer/distilroberta-base-ddlm/resolve/main/flax_model.msgpack
328 MB
- Xet hash:
- 10cf0cbd3b85352a623e91a5c814e05e3821cef0d262b2e75929ee1e7e1d78fc
- Size of remote file:
- 328 MB
- SHA256:
- ebc3b276a96456d2a2e1cf52006851f6f470710f4fad4a57f0e9275a00ec622a
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