Instructions to use epiphacc/csabstract-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use epiphacc/csabstract-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="epiphacc/csabstract-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("epiphacc/csabstract-classification") model = AutoModelForSequenceClassification.from_pretrained("epiphacc/csabstract-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from epiphacc/csabstract-classification: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/epiphacc/csabstract-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://epiphacc/csabstract-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/epiphacc/csabstract-classification/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- e3388aa51f412039b7143c6090575c1415162bf00dfc113fb2ec5117de0a38e9
- Size of remote file:
- 268 MB
- SHA256:
- fb702f08c02bc82350478d6854a4b4532b22e404b35a7e5823b22763e4648691
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