Instructions to use nqcccccc/phobert-multilabel-post-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nqcccccc/phobert-multilabel-post-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nqcccccc/phobert-multilabel-post-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nqcccccc/phobert-multilabel-post-classification") model = AutoModelForSequenceClassification.from_pretrained("nqcccccc/phobert-multilabel-post-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nqcccccc/phobert-multilabel-post-classification: direct link, hf CLI and curl.
- Browser
- Download file 540 MB
-
https://huggingface.co/nqcccccc/phobert-multilabel-post-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nqcccccc/phobert-multilabel-post-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nqcccccc/phobert-multilabel-post-classification/resolve/main/pytorch_model.bin
540 MB
- Xet hash:
- aaf8765acfc6d21d1ef075db682826a72fe2b6f883eae3e9de227c8efca8dcb0
- Size of remote file:
- 540 MB
- SHA256:
- 129274a464557935d62bedbbdb8f0a30540f0a95b22e2d05a0119ea6a5d2790b
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