Instructions to use datnth1709/Phobert-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use datnth1709/Phobert-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="datnth1709/Phobert-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("datnth1709/Phobert-classifier") model = AutoModelForMaskedLM.from_pretrained("datnth1709/Phobert-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from datnth1709/Phobert-classifier: direct link, hf CLI and curl.
- Browser
- Download file 134 Bytes
-
https://huggingface.co/datnth1709/Phobert-classifier/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://datnth1709/Phobert-classifier/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/datnth1709/Phobert-classifier/resolve/main/flax_model.msgpack
134 Bytes
- Xet hash:
- d72065cd90bffd6261cb228715cc9f61b2a996c963066e32151e07232f20ddb5
- Size of remote file:
- 134 Bytes
- SHA256:
- 3e2175c6ef2b2b841682ec586bc5d9d55a98f83e0b234d97d1054b633e14127b
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