Instructions to use paritc/multilabelclassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paritc/multilabelclassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="paritc/multilabelclassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("paritc/multilabelclassification") model = AutoModelForSequenceClassification.from_pretrained("paritc/multilabelclassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from paritc/multilabelclassification: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/paritc/multilabelclassification/resolve/main/training_args.bin
- Command line
-
hf download hf://paritc/multilabelclassification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/paritc/multilabelclassification/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 80f21311cde00e51fd881e7e6e85987a9a8f07e2e20fcb778240763b5c0c4c8f
- Size of remote file:
- 5.37 kB
- SHA256:
- 20ee680771d8394d2d18fa65b660b1270be191ab486c48af1149981e5895fc15
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