Instructions to use moosemandada/tiny-ner-probe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moosemandada/tiny-ner-probe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="moosemandada/tiny-ner-probe")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("moosemandada/tiny-ner-probe") model = AutoModelForTokenClassification.from_pretrained("moosemandada/tiny-ner-probe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from moosemandada/tiny-ner-probe: direct link, hf CLI and curl.
- Browser
- Download file 238 kB
-
https://huggingface.co/moosemandada/tiny-ner-probe/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://moosemandada/tiny-ner-probe/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/moosemandada/tiny-ner-probe/resolve/main/flax_model.msgpack
238 kB
- Xet hash:
- df4a4228e7d572130f3719a132d7fd9a4af69a87a2deb3a6508db5c44331c85e
- Size of remote file:
- 238 kB
- SHA256:
- 185ebff07fc9844ac30964ff0527592b0b7a1a06d688bef1b296871315e6c891
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