Instructions to use PlatschLena/support-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PlatschLena/support-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PlatschLena/support-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PlatschLena/support-classification") model = AutoModelForSequenceClassification.from_pretrained("PlatschLena/support-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from PlatschLena/support-classification: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/PlatschLena/support-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://PlatschLena/support-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/PlatschLena/support-classification/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- 61b64e584d1ac62d936c4b4e51e241f85fecec8a9dad1dc5eda71a4c3b85803f
- Size of remote file:
- 440 MB
- SHA256:
- f8027db4137e7d74305cb06a3273664046eb2f7fa4f5f77184e3f6187f613cc2
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