Instructions to use reaprtripr/distill-bert-csn-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reaprtripr/distill-bert-csn-python with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="reaprtripr/distill-bert-csn-python")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("reaprtripr/distill-bert-csn-python") model = AutoModelForMaskedLM.from_pretrained("reaprtripr/distill-bert-csn-python", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from reaprtripr/distill-bert-csn-python: direct link, hf CLI and curl.
- Browser
- Download file 363 MB
-
https://huggingface.co/reaprtripr/distill-bert-csn-python/resolve/refs%2Fpr%2F1/tf_model.h5
- Command line
-
hf download hf://reaprtripr/distill-bert-csn-python@refs/pr/1/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/reaprtripr/distill-bert-csn-python/resolve/refs%2Fpr%2F1/tf_model.h5
363 MB
- Xet hash:
- f4ca36f1b1922b5886ac49b2fe908bbc116294659ac353a5160dbfbc06b3d1df
- Size of remote file:
- 363 MB
- SHA256:
- f4ea4d042f0b8b868a8a16647ba799ea602815cf2463f2023fd146ef08669494
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