Instructions to use gianma/classifierEUtopLevelBDWdIntRebalanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gianma/classifierEUtopLevelBDWdIntRebalanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gianma/classifierEUtopLevelBDWdIntRebalanced")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gianma/classifierEUtopLevelBDWdIntRebalanced") model = AutoModelForSequenceClassification.from_pretrained("gianma/classifierEUtopLevelBDWdIntRebalanced", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gianma/classifierEUtopLevelBDWdIntRebalanced: direct link, hf CLI and curl.
- Browser
- Download file 444 MB
-
https://huggingface.co/gianma/classifierEUtopLevelBDWdIntRebalanced/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gianma/classifierEUtopLevelBDWdIntRebalanced/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gianma/classifierEUtopLevelBDWdIntRebalanced/resolve/main/pytorch_model.bin
444 MB
- Xet hash:
- 692d3dcce0f711012978747d06705ccc58c890bd6920ce3db9e6c6e4efc0a093
- Size of remote file:
- 444 MB
- SHA256:
- fb37f73fc9af1b75aad95002d87d498cde905051def75e863903271de68644ad
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