Instructions to use hamishivi/fixed-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hamishivi/fixed-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hamishivi/fixed-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hamishivi/fixed-roberta-base") model = AutoModel.from_pretrained("hamishivi/fixed-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from hamishivi/fixed-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/hamishivi/fixed-roberta-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://hamishivi/fixed-roberta-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/hamishivi/fixed-roberta-base/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 71fa17faa09bbc369bf4996f0c475e7231ef5ad910803ac60f2a05495ca83849
- Size of remote file:
- 499 MB
- SHA256:
- 3d436a8da1a189d39f2116e58a6d08f6d45435b10670d005c601e7d2539adae6
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