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 training_args.bin from nqcccccc/phobert-multilabel-post-classification: direct link, hf CLI and curl.
- Browser
- Download file 3.25 kB
-
https://huggingface.co/nqcccccc/phobert-multilabel-post-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://nqcccccc/phobert-multilabel-post-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nqcccccc/phobert-multilabel-post-classification/resolve/main/training_args.bin
3.25 kB
- Xet hash:
- 980c6be5096602c0d3aecbeb322ac1315c71dab7999401c051958d89d839e10f
- Size of remote file:
- 3.25 kB
- SHA256:
- 9596fa5ef40f91ccc101c48f83b62ea4c191cfdf90c373d3a0bf306c8cf8a0a4
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