Instructions to use nqcccccc/phobert-textclassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nqcccccc/phobert-textclassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nqcccccc/phobert-textclassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nqcccccc/phobert-textclassification") model = AutoModelForSequenceClassification.from_pretrained("nqcccccc/phobert-textclassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nqcccccc/phobert-textclassification: direct link, hf CLI and curl.
- Browser
- Download file 540 MB
-
https://huggingface.co/nqcccccc/phobert-textclassification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nqcccccc/phobert-textclassification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nqcccccc/phobert-textclassification/resolve/main/pytorch_model.bin
540 MB
- Xet hash:
- 2096f9694ab10478334ed9a4601a7115afb7f0087cae5b8067d773d7f2bec1f5
- Size of remote file:
- 540 MB
- SHA256:
- 7a0e67767b5bbc9491b8987709b99b135b8dc019c0dbf2ac4703a573664513d5
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