Instructions to use asparius/convbert-combined-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asparius/convbert-combined-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="asparius/convbert-combined-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("asparius/convbert-combined-large") model = AutoModelForSequenceClassification.from_pretrained("asparius/convbert-combined-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from asparius/convbert-combined-large: direct link, hf CLI and curl.
- Browser
- Download file 430 MB
-
https://huggingface.co/asparius/convbert-combined-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://asparius/convbert-combined-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/asparius/convbert-combined-large/resolve/main/pytorch_model.bin
430 MB
- Xet hash:
- 2fb08e782c721f0acf024df1f4323b78626b103052533946554b07a5bb5bb8f0
- Size of remote file:
- 430 MB
- SHA256:
- dfc7f08ddcfdfc56e05c3a6165d2367b0a71e788212b9a602c2f465a11eece2a
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