Instructions to use giganticode/StackOBERTflow-comments-small-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use giganticode/StackOBERTflow-comments-small-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="giganticode/StackOBERTflow-comments-small-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("giganticode/StackOBERTflow-comments-small-v1") model = AutoModelForMaskedLM.from_pretrained("giganticode/StackOBERTflow-comments-small-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from giganticode/StackOBERTflow-comments-small-v1: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/giganticode/StackOBERTflow-comments-small-v1/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://giganticode/StackOBERTflow-comments-small-v1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/giganticode/StackOBERTflow-comments-small-v1/resolve/main/flax_model.msgpack
334 MB
- Xet hash:
- a048540a3bc8c926a7c5c7f9bfc04037463c3295abd635664b5ca9265d9ad37d
- Size of remote file:
- 334 MB
- SHA256:
- a3b161b55b52acf9e30233c46fd43cef69d87768e8f49764740d84c13781c169
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