Instructions to use rafaelkallis/tickettagger-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rafaelkallis/tickettagger-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rafaelkallis/tickettagger-roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rafaelkallis/tickettagger-roberta") model = AutoModelForSequenceClassification.from_pretrained("rafaelkallis/tickettagger-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rafaelkallis/tickettagger-roberta: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/rafaelkallis/tickettagger-roberta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rafaelkallis/tickettagger-roberta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rafaelkallis/tickettagger-roberta/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 9e80370e733a0a1e3fd3b383831cb5da0fd0aa0fa1997d0af9f24fde1cb677d4
- Size of remote file:
- 499 MB
- SHA256:
- 151675873fc10df6ea18a93ffd026ad15c7e38a4841e47ac06d3aa93f9950330
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