Instructions to use Omar2027/Author_identification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Omar2027/Author_identification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Omar2027/Author_identification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Omar2027/Author_identification") model = AutoModelForMaskedLM.from_pretrained("Omar2027/Author_identification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Omar2027/Author_identification: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/Omar2027/Author_identification/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Omar2027/Author_identification/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Omar2027/Author_identification/resolve/main/flax_model.msgpack
541 MB
- Xet hash:
- 3e3555d4dd348f802ed033833e72ee1fbab149f5429ea69b16d1502b57999330
- Size of remote file:
- 541 MB
- SHA256:
- de41a4a649abc9d4c4cae41d29a921d41f301b2cdc51692a8d238832c5a2987b
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